pwc-archive/papers-with-abstracts huggingface dataset:
Machine Learning Interventions for Weed Detection using Multispectral Imagery and Unmanned Aerial Vehicles -- A Systematic Review
Authors: Drishti Goel, Bhavya Kapur, Prem Prakash Vuppuluri
Date: 2024-08-13 00:00:00
Tasks: Management

The growth of weeds poses a significant challenge to agricultural productivity, necessitating efficient and accurate weed detection and management strategies. The combination of multispectral imaging and drone technology has emerged as a promising approach to tackle this issue, enabling rapid and cost-effective monitoring of large agricultural fields. This systematic review surveys and evaluates the state-of-the-art in machine learning interventions for weed detection that utilize multispectral images captured by unmanned aerial vehicles. The study describes the various models used for training, features extracted from multi-spectral data, their efficiency and effect on the results, the performance analysis parameters, and also the current challenges faced by researchers in this domain. The review was conducted in accordance with the PRISMA guidelines. Three sources were used to obtain the relevant material, and the screening and data extraction were done on the COVIDENCE platform. The search string resulted in 600 papers from all sources. The review also provides insights into potential research directions and opportunities for further advancements in the field. These insights would serve as a valuable guide for researchers, agricultural scientists, and practitioners in developing precise and sustainable weed management strategies to enhance agricultural productivity and minimize ecological impact.
Hardware Architecture Design of Model-Based Image Reconstruction Towards Palm-size Photoacoustic Tomography
Authors: Yuwei Zheng, Zijian Gao, Yuting Shen, Jiadong Zhang, Daohuai Jiang, Fengyu Liu, Feng Gao, Fei Gao
Date: 2024-08-12 00:00:00
Tasks: CPU, Image Reconstruction

Photoacoustic (PA) imaging technology combines the advantages of optical imaging and ultrasound imaging, showing great potential in biomedical applications. Many preclinical studies and clinical applications urgently require fast, high-quality, low-cost and portable imaging system. Translating advanced image reconstruction algorithms into hardware implementations is highly desired. However, existing iterative PA image reconstructions, although exhibit higher accuracy than delay-and-sum algorithm, suffer from high computational cost. In this paper, we introduce a model-based hardware acceleration architecture based on superposed Wave (s-Wave) for palm-size PA tomography (palm-PAT), aiming at enhancing both the speed and performance of image reconstruction at a much lower system cost. To achieve this, we propose an innovative data reuse method that significantly reduces hardware storage resource consumption. We conducted experiments by FPGA implementation of the algorithm, using both phantoms and in vivo human finger data to verify the feasibility of the proposed method. The results demonstrate that our proposed architecture can substantially reduce system cost while maintaining high imaging performance. The hardware-accelerated implementation of the model-based algorithm achieves a speedup of up to approximately 270 times compared to the CPU, while the corresponding energy efficiency ratio is improved by more than 2700 times.
Toward Pedestrian Head Tracking: A Benchmark Dataset and an Information Fusion Network
Authors: Kailai Sun, Xinwei Wang, Shaobo Liu, Qianchuan Zhao, Gao Huang, Chang Liu
Date: 2024-08-12 00:00:00
Tasks: Autonomous Driving, Collision Avoidance, Head Detection, Optical Flow Estimation, Pedestrian Detection, Robot Navigation

Pedestrian detection and tracking in crowded video sequences have a wide range of applications, including autonomous driving, robot navigation and pedestrian flow surveillance. However, detecting and tracking pedestrians in high-density crowds face many challenges, including intra-class occlusions, complex motions, and diverse poses. Although deep learning models have achieved remarkable progress in head detection, head tracking datasets and methods are extremely lacking. Existing head datasets have limited coverage of complex pedestrian flows and scenes (e.g., pedestrian interactions, occlusions, and object interference). It is of great importance to develop new head tracking datasets and methods. To address these challenges, we present a Chinese Large-scale Cross-scene Pedestrian Head Tracking dataset (Cchead) and a Multi-Source Information Fusion Network (MIFN). Our dataset has features that are of considerable interest, including 10 diverse scenes of 50,528 frames with over 2,366,249 heads and 2,358 tracks annotated. Our dataset contains diverse human moving speeds, directions, and complex crowd pedestrian flows with collision avoidance behaviors. We provide a comprehensive analysis and comparison with existing state-of-the-art (SOTA) algorithms. Moreover, our MIFN is the first end-to-end CNN-based head detection and tracking network that jointly trains RGB frames, pixel-level motion information (optical flow and frame difference maps), depth maps, and density maps in videos. Compared with SOTA pedestrian detection and tracking methods, MIFN achieves superior performance on our Cchead dataset. We believe our datasets and baseline will become valuable resources towards developing pedestrian tracking in dense crowds.
Performance and Non-adversarial Robustness of the Segment Anything Model 2 in Surgical Video Segmentation
Authors: Yiqing Shen, Hao Ding, Xinyuan Shao, Mathias Unberath
Date: 2024-08-07 00:00:00
Tasks: Adversarial Robustness, Image Segmentation, Interactive Segmentation, Non-Adversarial Robustness, Segmentation, Semantic Segmentation, Video Segmentation, Video Semantic Segmentation, Zero-shot Generalization

Fully supervised deep learning (DL) models for surgical video segmentation have been shown to struggle with non-adversarial, real-world corruptions of image quality including smoke, bleeding, and low illumination. Foundation models for image segmentation, such as the segment anything model (SAM) that focuses on interactive prompt-based segmentation, move away from semantic classes and thus can be trained on larger and more diverse data, which offers outstanding zero-shot generalization with appropriate user prompts. Recently, building upon this success, SAM-2 has been proposed to further extend the zero-shot interactive segmentation capabilities from independent frame-by-frame to video segmentation. In this paper, we present a first experimental study evaluating SAM-2's performance on surgical video data. Leveraging the SegSTRONG-C MICCAI EndoVIS 2024 sub-challenge dataset, we assess SAM-2's effectiveness on uncorrupted endoscopic sequences and evaluate its non-adversarial robustness on videos with corrupted image quality simulating smoke, bleeding, and low brightness conditions under various prompt strategies. Our experiments demonstrate that SAM-2, in zero-shot manner, can achieve competitive or even superior performance compared to fully-supervised deep learning models on surgical video data, including under non-adversarial corruptions of image quality. Additionally, SAM-2 consistently outperforms the original SAM and its medical variants across all conditions. Finally, frame-sparse prompting can consistently outperform frame-wise prompting for SAM-2, suggesting that allowing SAM-2 to leverage its temporal modeling capabilities leads to more coherent and accurate segmentation compared to frequent prompting.
Spectrum Prediction With Deep 3D Pyramid Vision Transformer Learning
Authors: Guangliang Pan, Qihui Wu, Bo Zhou, Jie Li, Wei Wang, Guoru Ding, David K. Y. Yau
Date: 2024-08-13 00:00:00
Tasks: Transfer Learning

In this paper, we propose a deep learning (DL)-based task-driven spectrum prediction framework, named DeepSPred. The DeepSPred comprises a feature encoder and a task predictor, where the encoder extracts spectrum usage pattern features, and the predictor configures different networks according to the task requirements to predict future spectrum. Based on the Deep- SPred, we first propose a novel 3D spectrum prediction method combining a flow processing strategy with 3D vision Transformer (ViT, i.e., Swin) and a pyramid to serve possible applications such as spectrum monitoring task, named 3D-SwinSTB. 3D-SwinSTB unique 3D Patch Merging ViT-to-3D ViT Patch Expanding and pyramid designs help the model accurately learn the potential correlation of the evolution of the spectrogram over time. Then, we propose a novel spectrum occupancy rate (SOR) method by redesigning a predictor consisting exclusively of 3D convolutional and linear layers to serve possible applications such as dynamic spectrum access (DSA) task, named 3D-SwinLinear. Unlike the 3D-SwinSTB output spectrogram, 3D-SwinLinear projects the spectrogram directly as the SOR. Finally, we employ transfer learning (TL) to ensure the applicability of our two methods to diverse spectrum services. The results show that our 3D-SwinSTB outperforms recent benchmarks by more than 5%, while our 3D-SwinLinear achieves a 90% accuracy, with a performance improvement exceeding 10%.
Chirped DFT-s-OFDM: A new single-carrier waveform with enhanced LMMSE noise suppression
Authors: Yujie Liu, Yong Liang Guan, David González G., Halim Yanikomeroglu
Date: 2024-08-13 00:00:00
Tasks: Diversity

In this paper, a new single-carrier waveform, called chirped discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM), is proposed for the sixth generation of communications. As the initial study on this waveform, its performance is analyzed and evaluated in single-user uplink multiple access communications, using a delay-Doppler channel model with independent and identically distributed path amplitudes and different integer delays. By chirping DFT-s-OFDM in the time domain, it maintains the low peak-to-average-power ratio of DFT-s-OFDM. Thanks to full-band transmission and symbols retransmission enabled by chirping and discrete Fourier transform precoding, it enhances noise suppression of linear minimum mean square error equalization. By using pairwise error probability analysis, the derived bit error rate upper bound is close to the simulated ones and the diversity order analysis confirms that it achieves full frequency diversity.
Sum Rate Maximization for Movable Antenna Enabled Uplink NOMA
Authors: Nianzu Li, Peiran Wu, Boyu Ning, Lipeng Zhu
Date: 2024-08-13 00:00:00
Tasks: Position

Movable antenna (MA) has been recently proposed as a promising candidate technology for the next generation wireless communication systems due to its significant capability of reconfiguring wireless channels via antenna movement. In this letter, we study an MA-enabled uplink non-orthogonal multiple access (NOMA) system, where each user is equipped with a single MA. Our objective is to maximize the users' sum rate by jointly optimizing the MAs' positions, the decoding order and the power control. To solve this non-convex problem, we equivalently transform it into two tractable subproblems. First, we use the successive convex approximation (SCA) to find a locally optimal solution for the antenna position optimization subproblem. Next, we derive the closed-form optimal solution of the decoding order and power control subproblem. Numerical results show that our proposed MA-enabled NOMA system can significantly enhance the sum rate compared to fixed-position antenna (FPA) systems and orthogonal multiple access (OMA) systems.
Joint Source-Channel Optimization for UAV Video Coding and Transmission
Authors: Kesong Wu, Xianbin Cao, Peng Yang, Haijun Zhang, Tony Q. S. Quek, Dapeng Oliver Wu
Date: 2024-08-13 00:00:00

This paper is concerned with unmanned aerial vehicle (UAV) video coding and transmission in scenarios such as emergency rescue and environmental monitoring. Unlike existing methods of modeling UAV video source coding and channel transmission separately, we investigate the joint source-channel optimization issue for video coding and transmission. Particularly, we design eight-dimensional delay-power-rate-distortion models in terms of source coding and channel transmission and characterize the correlation between video coding and transmission, with which a joint source-channel optimization problem is formulated. Its objective is to minimize end-to-end distortion and UAV power consumption by optimizing fine-grained parameters related to UAV video coding and transmission. This problem is confirmed to be a challenging sequential-decision and non-convex optimization problem. We therefore decompose it into a family of repeated optimization problems by Lyapunov optimization and design an approximate convex optimization scheme with provable performance guarantees to tackle these problems. Based on the theoretical transformation, we propose a Lyapunov repeated iteration (LyaRI) algorithm. Both objective and subjective experiments are conducted to comprehensively evaluate the performance of LyaRI. The results indicate that, compared to its counterparts, LyaRI achieves better video quality and stability performance, with a 47.74% reduction in the variance of the obtained encoding bitrate.
Pilot-Aided Joint Time Synchronization and Channel Estimation for OTFS
Authors: Jiazheng Sun, Peng Yang, Xianbin Cao, Zehui Xiong, Haijun Zhang, Tony Q. S. Quek
Date: 2024-08-08 00:00:00

This letter proposes a pilot-aided joint time synchronization and channel estimation (JTSCE) algorithm for orthogonal time frequency space (OTFS) systems. Unlike existing algorithms, JTSCE employs a maximum length sequence (MLS) rather than an isolated signal as the pilot. Distinctively, JTSCE explores MLS's autocorrelation properties to estimate timing offset and channel delay taps. After obtaining delay taps, closed-form expressions of Doppler and channel gain for each propagation path are derived. Simulation results indicate that, compared to its counterpart, JTSCE achieves better bit error rate performance, close to that with perfect time synchronization and channel state information.
Line Spectral Estimation with Unlimited Sensing
Authors: Hongwei Wang, Jun Fang, Hongbin Li, Geert Leus
Date: 2024-08-13 00:00:00
Tasks: Quantization

In the paper, we consider the line spectral estimation problem in an unlimited sensing framework (USF), where a modulo analog-to-digital converter (ADC) is employed to fold the input signal back into a bounded interval before quantization. Such an operation is mathematically equivalent to taking the modulo of the input signal with respect to the interval. To overcome the noise sensitivity of higher-order difference-based methods, we explore the properties of the first-order difference of modulo samples, and develop two line spectral estimation algorithms based on first-order difference, which are robust against noise. Specifically, we show that, with a high probability, the first-order difference of the original samples is equivalent to that of the modulo samples. By utilizing this property, line spectral estimation is solved via a robust sparse signal recovery approach. The second algorithms is built on our finding that, with a sufficiently high sampling rate, the first-order difference of the original samples can be decomposed as a sum of the first-order difference of the modulo samples and a sequence whose elements are confined to be three possible values. This decomposition enables us to formulate the line spectral estimation problem as a mixed integer linear program that can be efficiently solved. Simulation results show that both proposed methods are robust against noise and achieve a significant performance improvement over the higher-order difference-based method.
Orbital-Angular-Momentum Versus MIMO: Orthogonality, Degree of Freedom,and Capacity
Authors: Haiyue Jing, Wenchi Cheng, Xiang-Gen Xia, Hailin Zhang
Date: 2024-08-13 00:00:00

The plane wave based wireless communications have becoming more and more matured, along with the well utilization of the traditional resources such as time and frequency. To further increase the capacity for rapidly increasing capacity demand of wireless communications, it is potential to use the twist wave, which has the orbital angular momentum (OAM). In this paper, we discuss the OAM based wireless communications in the aspect of orthogonality, degree of freedom (DoF), and capacity, where both the transmitter and the receiver use uniform circular array (UCA) antennas. In particular, we compare OAM based wireless communications with multiple-input-multiple-output (MIMO) based wireless communications in terms of DoF and capacity. Numerical results are presented to validate and evaluate that the DoF of OAM based wireless communications is greater than or equal to that of correlated MIMO based wireless communications when the transmitter and the receiver antennas are aligned well. The OAM based wireless communications can achieve larger capacity than the correlated MIMO in high signal-to-noise ratio (SNR) region under line-of-sight scenario.
Breaking Limits of Line-of-Sight MIMO Capacity in 6G Wireless Communications
Authors: Haiyue Jing, Wenchi Cheng, Wei zhang
Date: 2024-08-13 00:00:00

Multiple-input-multiple-output (MIMO) has been proved its success for the fourth generation (4G) long term evolution (LTE) and is one of the key technical enablers for evolved mobile broadband (eMBB) in the fifth generation (5G) wireless communications. However, along with the number of antennas eventually increased to be extremely large and one-hop communication distance gradually reduced, how to significantly increase the capacity for line-of-sight (LOS) MIMO becomes more and more urgent. In this article, we introduce the quasi-fractal uniform circular array (QF-UCA) antenna structure based MIMO wireless communications, which can adequately exploit the potential of MIMO in LOS channel and greatly increase the capacity with low complexity demodulation schemes. Specifically, three advantages regarding QF-UCA based LOS MIMO are reviewed. Then, research challenges on transceiver alignment, low-rank channel matrix, extended dimensions of QF-UCA, maximum number of orthogonal streams, and the corresponding potential solutions are discussed. Compared with traditional scattering-depended MIMO communications, the QF-UCA based LOS MIMO wireless communication can achieve high-efficient transmission in LOS channel.
Fast Transceiver Design for RIS-Assisted MIMO mmWave Wireless Communications
Authors: Haiyue Jing, Wenchi Cheng, Xiang-Gen Xia
Date: 2024-08-13 00:00:00

Due to high bandwidth and small antenna size, millimeter-wave (mmWave) integrated line-of-sight (LOS) multiple-input-multiple-output (MIMO) systems have attracted much attention. Reconfigurable intelligent surfaces (RISs), which have the potential to change the characteristics of incident electromagnetic waves with low power cost, can improve the performance or the MIMO mmWave wireless communications. Uniform circular array (UCA) is an effective antenna structure with low complexity transceiver. In this paper, UCA based RIS-assisted MIMO mmWave wireless communications with transmit UCA, the RIS UCAs, and receive UCA are investigated. Since the rotation angles between the transceiver make the channel matrix noncirculant, an algorithm is developed to derive the ranges of the rotation angles based on an acceptable error and reduce the impact of rotation angles on channel matrix. Then, we propose a low-complexity precoding scheme at the transmitter, phase designs at the RIS UCAs, and a phase compensation scheme at the receiver, which can convert the channel matrix into an equivalent circulant channel matrix with a small error. Then, a fast symbol-wise maximum likelihood (ML) detection scheme is proposed to recover the signals with low computational complexity. Simulation results are presented to illustrate the theory.
Can Wireless Environmental Information Decrease Pilot Overhead: A CSI Prediction Example
Authors: Lianzheng Shi, Jianhua Zhang, Li Yu, Yuxiang Zhang, Zhen Zhang, Yichen Cai, Guangyi Liu
Date: 2024-08-13 00:00:00
Tasks: Prediction

Channel state information (CSI) is crucial for massive multi-input multi-output (MIMO) system. As the antenna scale increases, acquiring CSI results in significantly higher system overhead. In this letter, we propose a novel channel prediction method which utilizes wireless environmental information with pilot pattern optimization for CSI prediction (WEI-CSIP). Specifically, scatterers around the mobile station (MS) are abstracted from environmental information using multiview images. Then, an environmental feature map is extracted by a convolutional neural network (CNN). Additionally, the deep probabilistic subsampling (DPS) network acquires an optimal fixed pilot pattern. Finally, a CNN-based channel prediction network is designed to predict the complete CSI, using the environmental feature map and partial CSI. Simulation results show that the WEI-CSIP can reduce pilot overhead from 1/5 to 1/8, while improving prediction accuracy with normalized mean squared error reduced to 0.0113, an improvement of 83.2% compared to traditional channel prediction methods.
Optimal Preprocessing for Joint Detection and Classification of Wireless Communication Signals in Congested Spectrum Using Computer Vision Methods
Authors: Xiwen Kang, Hua-mei Chen, Genshe Chen, Kuo-Chu Chang, Thomas M. Clemons
Date: 2024-08-13 00:00:00

The joint detection and classification of RF signals has been a critical problem in the field of wideband RF spectrum sensing. Recent advancements in deep learning models have revolutionized this field, remarkably through the application of state-of-the-art computer vision algorithms such as YOLO (You Only Look Once) and DETR (Detection Transformer) to the spectrogram images. This paper focuses on optimizing the preprocessing stage to enhance the performance of these computer vision models. Specifically, we investigated the generation of training spectrograms via the classical Short-Time Fourier Transform (STFT) approach, examining four classical STFT parameters: FFT size, window type, window length, and overlapping ratio. Our study aims to maximize the mean average precision (mAP) scores of YOLOv10 models in detecting and classifying various digital modulation signals within a congested spectrum environment. Firstly, our results reveal that additional zero padding in FFT does not enhance detection and classification accuracy and introduces unnecessary computational cost. Secondly, our results indicated that there exists an optimal window size that balances the trade-offs between and the time and frequency resolution, with performance losses of approximately 10% and 30% if the window size is four or eight times off from the optimal. Thirdly, regarding the choice of window functions, the Hamming window yields optimal performance, with non-optimal windows resulting in up to a 10% accuracy loss. Finally, we found a 10% accuracy score performance gap between using 10% and 90% overlap. These findings highlight the potential for significant performance improvements through optimized spectrogram parameters when applying computer vision models to the problem of wideband RF spectrum sensing.