pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing Edge Intelligence with Highly Discriminant LNT Features
Authors: Xinyu Wang, Vinod K. Mishra, C. -C. Jay Kuo
Date: 2023-12-19 00:00:00
Tasks: Binary Classification, Representation Learning

AI algorithms at the edge demand smaller model sizes and lower computational complexity. To achieve these objectives, we adopt a green learning (GL) paradigm rather than the deep learning paradigm. GL has three modules: 1) unsupervised representation learning, 2) supervised feature learning, and 3) supervised decision learning. We focus on the second module in this work. In particular, we derive new discriminant features from proper linear combinations of input features, denoted by x, obtained in the first module. They are called complementary and raw features, respectively. Along this line, we present a novel supervised learning method to generate highly discriminant complementary features based on the least-squares normal transform (LNT). LNT consists of two steps. First, we convert a C-class classification problem to a binary classification problem. The two classes are assigned with 0 and 1, respectively. Next, we formulate a least-squares regression problem from the N-dimensional (N-D) feature space to the 1-D output space, and solve the least-squares normal equation to obtain one N-D normal vector, denoted by a1. Since one normal vector is yielded by one binary split, we can obtain M normal vectors with M splits. Then, Ax is called an LNT of x, where transform matrix A in R^{M by N} by stacking aj^T, j=1, ..., M, and the LNT, Ax, can generate M new features. The newly generated complementary features are shown to be more discriminant than the raw features. Experiments show that the classification performance can be improved by these new features.
A Cascaded Neural Network System For Rating Student Performance In Surgical Knot Tying Simulation
Authors: Yunzhe Xue, Olanrewaju Eletta, Justin W. Ady, Nell M. Patel, Advaith Bongu, Usman Roshan
Date: 2023-12-09 00:00:00

As part of their training all medical students and residents have to pass basic surgical tasks such as knot tying, needle-passing, and suturing. Their assessment is typically performed in the operating room by surgical faculty where mistakes and failure by the student increases the operation time and cost. This evaluation is quantitative and has a low margin of error. Simulation has emerged as a cost effective option but it lacks assessment or requires additional expensive hardware for evaluation. Apps that provide training videos on surgical knot trying are available to students but none have evaluation. We propose a cascaded neural network architecture that evaluates a student's performance just from a video of themselves simulating a surgical knot tying task. Our model converts video frame images into feature vectors with a pre-trained deep convolutional network and then models the sequence of frames with a temporal network. We obtained videos of medical students and residents from the Robert Wood Johnson Hospital performing knot tying on a standardized simulation kit. We manually annotated each video and proceeded to do a five-fold cross-validation study on them. Our model achieves a median precision, recall, and F1-score of 0.71, 0.66, and 0.65 respectively in determining the level of knot related tasks of tying and pushing the knot. Our mean precision score averaged across different probability thresholds is 0.8. Both our F1-score and mean precision score are 8% and 30% higher than that of a recently published study for the same problem. We expect the accuracy of our model to further increase as we add more training videos to the model thus making it a practical solution that students can use to evaluate themselves.
Large-scale Graph Representation Learning of Dynamic Brain Connectome with Transformers
Authors: Byung-Hoon Kim, JungWon Choi, Eunggu Yun, Kyungsang Kim, Xiang Li, Juho Lee
Date: 2023-12-04 00:00:00
Tasks: Functional Connectivity, Gender Classification, Graph Representation Learning, Representation Learning

Graph Transformers have recently been successful in various graph representation learning tasks, providing a number of advantages over message-passing Graph Neural Networks. Utilizing Graph Transformers for learning the representation of the brain functional connectivity network is also gaining interest. However, studies to date have underlooked the temporal dynamics of functional connectivity, which fluctuates over time. Here, we propose a method for learning the representation of dynamic functional connectivity with Graph Transformers. Specifically, we define the connectome embedding, which holds the position, structure, and time information of the functional connectivity graph, and use Transformers to learn its representation across time. We perform experiments with over 50,000 resting-state fMRI samples obtained from three datasets, which is the largest number of fMRI data used in studies by far. The experimental results show that our proposed method outperforms other competitive baselines in gender classification and age regression tasks based on the functional connectivity extracted from the fMRI data.
Interference-Resilient OFDM Waveform Design with Subcarrier Interval Constraint for ISAC Systems
Authors: Qinghui Lu, Zhen Du, Zenghui Zhang
Date: 2023-12-26 00:00:00
Tasks: ISAC

Conventional orthogonal frequency division multiplexing (OFDM) waveform design in integrated sensing and communications (ISAC) systems usually selects the channels with high-frequency responses to transmit communication data, which does not fully consider the possible interference in the environment. To mitigate these adverse effects, we propose an optimization model by weighting between peak sidelobe level and communication data rate, with power and communication subcarrier interval constraints. To tackle the resultant nonconvex problem, an iterative adaptive cyclic minimization (ACM) algorithm is developed, where an adaptive iterative factor is introduced to improve convergence. Subsequently, the least squares algorithm is used to reduce the coefficient of variation of envelopes by further optimizing the phase of the OFDM waveform. Finally, the numerical simulations are provided to demonstrate the interference-resilient ability of the proposed OFDM strategy and the robustness of the ACM algorithm.
Blind Frequency-Domain Equalization Using Vector-Quantized Variational Autoencoders
Authors: Jinxiang Song, Vincent Lauinger, Christian Häger, Jochen Schröder, Alexandre Graell i Amat, Laurent Schmalen, Henk Wymeersch
Date: 2023-12-26 00:00:00

We propose a novel frequency-domain blind equalization scheme for coherent optical communications. The method is shown to achieve similar performance to its recently proposed time-domain counterpart with lower computational complexity, while outperforming the commonly used CMA-based equalizers.
Measurements and modelisation of shadowing cross-correlations between two base-stations
Authors: Karim Zayana, Bertrand Guisnet
Date: 2023-12-26 00:00:00

Measurements in macrodiversity situations have been performed. Crosscorrelation of shadowing terms between two basestations and a mobile have been computed. It results that high cross-correlation coefficients may appear in practice. Afterwards, we propose a simple shadowing model, integrating both its autocorrelation and cross-correlation properties. Eventually, this model is used to compute simulations in some typical network cases.
Mesure simultan{é}e des r{é}ponses impulsionnelles en macrodiversit{é}
Authors: Karim Zayana, Daniel Duponteil
Date: 2023-12-26 00:00:00
Tasks: Diversity

Macroscopic diversity in radiomobile systems is a technique in which the terminal is in parallel connection with two base stations. It is a fundamental element in the operation of third generation mobile networks using CDMA. (IS 95, UMTS, etc.). A representation of the propagation phenomena involved requires joint modeling of the two transmission channels. The article presents a very simple method for simultaneously measuring their impulse responses, developed for this purpose. This method was the subject of a patent. It can more generally be applied to any number of channels and should allow the reuse of already existing single-channel sounders.
Reshaping the ISAC Tradeoff Under OFDM Signaling: A Probabilistic Constellation Shaping Approach
Authors: Zhen Du, Fan Liu, Yifeng Xiong, Tony Xiao Han, Yonina C. Eldar, Shi Jin
Date: 2023-12-26 00:00:00
Tasks: ISAC

Integrated sensing and communications is regarded as a key enabling technology in the sixth generation networks, where a unified waveform, such as orthogonal frequency division multiplexing (OFDM) signal, is adopted to facilitate both sensing and communications (S&C). However, the random communication data embedded in the OFDM signal results in severe variability in the sidelobes of its ambiguity function (AF), which leads to missed detection of weak targets and false detection of ghost targets, thereby impairing the sensing performance. Therefore, balancing between preserving communication capability (i.e., the randomness) while improving sensing performance remains a challenging task. To cope with this issue, we characterize the random AF of OFDM communication signals, and demonstrate that the AF variance is determined by the fourth-moment of the constellation amplitudes. Subsequently, we propose an optimal probabilistic constellation shaping (PCS) approach by maximizing the achievable information rate (AIR) under the fourth-moment, power and probability constraints, where the optimal input distribution may be numerically specified through a modified Blahut-Arimoto algorithm. To reduce the computational overheads, we further propose a heuristic PCS approach by actively controlling the value of the fourth-moment, without involving the communication metric in the optimization model, despite that the AIR is passively scaled with the variation of the input distribution. Numerical results show that both approaches strike a scalable performance tradeoff between S&C, where the superiority of the PCS-enabled constellations over conventional uniform constellations is also verified. Notably, the heuristic approach achieves very close performance to the optimal counterpart, at a much lower computational complexity.
DRL-Based Sidelobe Suppression for Multi-focus Reconfigurable Intelligent Surface
Authors: Wei Wang, Peizheng Li, Angela Doufexi, Mark A Beach
Date: 2023-12-24 00:00:00
Tasks: Deep Reinforcement Learning

Reconfigurable intelligent surface (RIS) technology is receiving significant attention as a key enabling technology for 6G communications, with much attention given to coverage infill and wireless power transfer. However, relatively little attention has been paid to the radiation pattern fidelity, for example, sidelobe suppression. When considering multi-user coverage infill, direct beam pattern synthesis using superposition can result in undesirable sidelobe levels. To address this issue, this paper introduces and applies deep reinforcement learning (DRL) as a means to optimize the far-field pattern, offering a 4dB reduction in the unwanted sidelobe levels, thereby improving energy efficiency and decreasing the co-channel interference levels.
A Belief Propagation Approach for Direct Multipath-Based SLAM
Authors: Mingchao Liang, Erik Leitinger, Florian Meyer
Date: 2023-12-24 00:00:00
Tasks: Simultaneous Localization and Mapping

In this work, we develop a multipath-based simultaneous localization and mapping (SLAM) method that can directly be applied to received radio signals. In existing multipath-based SLAM approaches, a channel estimator is used as a preprocessing stage that reduces data flow and computational complexity by extracting features related to multipath components (MPCs). We aim to avoid any preprocessing stage that may lead to a loss of relevant information. The presented method relies on a new statistical model for the data generation process of the received radio signal that can be represented by a factor graph. This factor graph is the starting point for the development of an efficient belief propagation (BP) method for multipath-based SLAM that directly uses received radio signals as measurements. Simulation results in a realistic scenario with a single-input single-output (SISO) channel demonstrate that the proposed direct method for radio-based SLAM outperforms state-of-the-art methods that rely on a channel estimator.
Exploiting Multipath Information for Integrated Localization and Sensing via PHD Filtering
Authors: Yinuo Du, Hanying Zhao, Yang Liu, Xinlei Yu, Yuan Shen
Date: 2023-12-24 00:00:00

Accurate localization and perception are pivotal for enhancing the safety and reliability of vehicles. However, current localization methods suffer from reduced accuracy when the line-of-sight (LOS) path is obstructed, or a combination of reflections and scatterings is present. In this paper, we present an integrated localization and sensing method that delivers superior performance in complex environments while being computationally efficient. Our method uniformly leverages various types of multipath components (MPCs) through the lens of random finite sets (RFSs), encompassing reflections, scatterings, and their combinations. This advancement eliminates the need for the multipath identification step and streamlines the filtering process by removing the necessity for distinct filters for different multipath types, a requirement that was critical in previous research. The simulation results demonstrate the superior performance of our method in both robustness and effectiveness, particularly in complex environments where the LOS MPC is obscured and in situations involving clutter and missed detection of MPC measurements.
A Novel Field-Free SOT Magnetic Tunnel Junction With Local VCMA-Induced Switching
Authors: Rui Zhou, Haiyang Zhang, Hao Wang, Jin He, Qijun Huang, Sheng Chang
Date: 2023-12-24 00:00:00

By integrating the local voltage-controlled magnetic anisotropy (VCMA) effect, Dzyaloshinskii-Moriya interaction (DMI) effect, and spin-orbit torque (SOT) effect, we propose a novel device structure for field-free magnetic tunnel junction (MTJ). Micromagnetic simulation shows that the device utilizes the chiral symmetry breaking caused by the DMI effect to induce a non-collinear spin texture under the influence of SOT current. This, combined with the perpendicular magnetic anisotropy (PMA) gradient generated by the local VCMA effect, enables deterministic switching of the MTJ state without an external field. The impact of variations in DMI strength and PMA gradient on the magnetization dynamics is analyzed.
Road-Aware Localization With Salient Feature Matching in Heterogeneous Networks
Authors: Lele Cong, Deshi Li, Kaitao Meng, Shuya Zhu
Date: 2023-12-24 00:00:00

Vehicle localization is essential for intelligent transportation. However, achieving low-latency vehicle localization without sacrificing precision is challenging. In this paper, we propose a road-aware localization mechanism in heterogeneous networks (HetNet), where distinct features of HetNet signals are extracted for two-spatial-scale position mapping, enabling low-latency positioning with high precision. Specifically, we propose a sequence segmentation method to extract the low-dimensional positioning space on two spatial scales. To represent roads and sub-segments according to HetNet signals, we propose a salient feature extraction method to eliminate redundant features and retain distinct features, thereby reducing feature-matching complexity and improving representation accuracy. Based on the extracted salient features, a two-spatial-scale localization algorithm is designed through salient feature matching, which can achieve low-latency road-aware localization. Furthermore, high-precision positioning is achieved by coordinate mapping based on curve fitting. Simulation results show that our mechanism can provide a low-latency and high-precision positioning service compared to the benchmark schemes.
LMMSE-based SIMO Receiver for Ultraviolet Scattering Communication with Nonlinear Conversion
Authors: Zanqiu Shen, Jianshe Ma, Ping Su
Date: 2023-12-22 00:00:00

Linear minimum mean square error (LMMSE) receivers are often applied in practical communication scenarios for single-input-multiple-output (SIMO) systems owing to their low computational complexity and competitive performance. However, their performance is only the best among all the linear receivers, as they minimize the bit mean square error (MSE) alone in linear space. To overcome this limitation, in this study, we propose an LMMSE receiver based on the measurements augmented by their nonlinear conversion for a photon-counting receiver, a photomultiplier tube, and an avalanche photodetector. The performance of the proposed LMMSE receiver is studied for different nonlinear conversions, numbers of receivers, and receiver types. The simulation results indicate that the Monte Carlo results are consistent with the analytical results and that the proposed LMMSE receiver outperforms the conventional one in terms of bit MSE and bit error rate. Accordingly, it can be concluded that to achieve a desired bit MSE, the proposed LMMSE-based nonlinear receiver not only reduces the need to increase the number of receivers but also reduces the bandwidth requirements.
AI Generated Signal for Wireless Sensing
Authors: Hanxiang He, Han Hu, Xintao Huan, Heng Liu, Jianping An, Shiwen Mao
Date: 2023-12-22 00:00:00
Tasks: Attribute, Denoising, Disentanglement

Deep learning has significantly advanced wireless sensing technology by leveraging substantial amounts of high-quality training data. However, collecting wireless sensing data encounters diverse challenges, including unavoidable data noise, limited data scale due to significant collection overhead, and the necessity to reacquire data in new environments. Taking inspiration from the achievements of AI-generated content, this paper introduces a signal generation method that achieves data denoising, augmentation, and synthesis by disentangling distinct attributes within the signal, such as individual and environment. The approach encompasses two pivotal modules: structured signal selection and signal disentanglement generation. Structured signal selection establishes a minimal signal set with the target attributes for subsequent attribute disentanglement. Signal disentanglement generation disentangles the target attributes and reassembles them to generate novel signals. Extensive experimental results demonstrate that the proposed method can generate data that closely resembles real-world data on two wireless sensing datasets, exhibiting state-of-the-art performance. Our approach presents a robust framework for comprehending and manipulating attribute-specific information in wireless sensing.
Material Classification of Recyclable Containers Using 60 GHz Radar
Authors: Tommy Albing, Rikard Nelander
Date: 2023-12-22 00:00:00
Tasks: Material Classification

Rather than sending used containers and materials to the landfill, recycling can help lower the human impact on the environment. However, manually sorting the mixture of incoming material can be both costly and potentially harmful to the person carrying out the task. In many cases, manual sorting could be replaced with automation, where a container is sorted by a machine, based on a classification of the container's material. In this paper, we propose a classification algorithm, using radar data, acquired with Acconeer's A121 60 GHz pulse coherent radar, for classifying liquid containers into one of the four classes metal, glass, plastic, or paper. The solution offers a cost-effective system with robust performance, able to predict the type of container with 98% accuracy.
Instantaneous Frequency Estimation in Multicomponent Signals in Case of Interference Based on the Prony Method
Authors: Basile Dubois-Bonnaire, Sylvain Meignen, Kévin Polisano
Date: 2023-12-22 00:00:00

In this paper, we develop a general method to estimate the instantaneous frequencies of the modes making up a multicomponent signal when the former exhibit interference in the time-frequency plane. In particular, studying the representation given by the spectrogram, we show that it is possible to characterize the interference between the modes using the Prony method, which enables us to build a novel instantaneous frequency estimator for the mode. The relevance of the proposed approach is demonstrated by comparing it with different stateof-the art techniques based on ridge detection.
On Smart Morphing Wing Aircraft Robust Adaptive Beamforming
Authors: Yizhen Jia, Hui Chen, Wen-Qin Wang, Jie Cheng
Date: 2023-12-22 00:00:00

The smart morphing wing aircraft (SMWA) is a highly adaptable platform that can be widely used for intelligent warfare due to its real-time variable structure. The flexible conformal array (FCA) is a vital detection component of SMWA, when the deformation parameters of FCA are mismatched or array elements are mutually coupled, detection performance will be degraded. To overcome this problem and ensure robust beamforming for FCA, deviations in array control parameters (ACPs) and array perturbations, the effect of mutual coupling in addition to looking-direction errors should be considered. In this paper, we propose a robust adaptive beamforming (RAB) algorithm by reconstructing a multi-domain interference plus noise covariance matrix (INCM) and estimating steering vector (SV) for FCA. We first reconstruct the INCM using multi-domain processing, including ACP and angular domains. Then, SV estimation is executed through an optimization procedure. Experimental results have shown that the proposed beamformer outperforms existing beamformers in various mismatch conditions and harsh environments, such as high interference-to-noise ratios, and mutual coupling of antennas.
FDA-MIMO-based Integrated Sensing and Communication System with Frequency Offset Permutation Index Modulation
Authors: Jiangwei Jian, Qimao Huang, Bang Huang, Wen-Qin Wang
Date: 2023-12-22 00:00:00
Tasks: Integrated sensing and communication, ISAC

Considering that frequency diverse array multiple-input multiple-output (FDA-MIMO) possesses extra range information to enhance sensing performance, this paper explores the FDA-MIMO-based integrated sensing and communication (ISAC) system. To reinforce the system communication capability, we propose the frequency offset permutation index modulation (FOPIM) scheme, which conveys extra information bits by selecting and permutating frequency offsets from a frequency offsets pool. For the system communication sub-functionality, considering the fact that the traditional maximum likelihood detection method suffers from high complexity and bit error rate (BER), the maximum likelihood-based two-stage detection (MLTSD) approach is presented to overcome this issue. For the system sensing sub-function, we employ the two-step maximum likelihood estimator (TSMLE) to stepwise estimate the angle and range of the interested target. Furthermore, we derive the closed-form expressions for the tight upper bound on the communication BER, along with the sensing Cram\'er-Rao bound (CRB). The simulation results validate the theoretical analysis, demonstrating that the proposed system exhibits lower BER and superior range resolution than independent MIMO communication and MIMO sensing modules.