pwc-archive/papers-with-abstracts huggingface dataset:
QuST: QuPath Extension for Integrative Whole Slide Image and Spatial Transcriptomics Analysis
Authors: Chao-Hui Huang, Sara Lichtarge, Diane Fernandez
Date: 2024-05-31 00:00:00

The integration of AI in digital pathology, particularly in whole slide image (WSI) and spatial transcriptomics (ST) analysis, holds immense potential for enhancing our understanding of diseases. Despite challenges such as training pattern preparation and resolution disparities, the convergence of these technologies can unlock new insights. We introduce QuST, a tool that bridges the gap between WSI and ST, underscoring the transformative power of this integrated approach in disease biology.
An Enhanced Encoder-Decoder Network Architecture for Reducing Information Loss in Image Semantic Segmentation
Authors: Zijun Gao, Qi Wang, Taiyuan Mei, Xiaohan Cheng, Yun Zi, Haowei Yang
Date: 2024-05-26 00:00:00
Tasks: Decoder, Semantic Segmentation

The traditional SegNet architecture commonly encounters significant information loss during the sampling process, which detrimentally affects its accuracy in image semantic segmentation tasks. To counter this challenge, we introduce an innovative encoder-decoder network structure enhanced with residual connections. Our approach employs a multi-residual connection strategy designed to preserve the intricate details across various image scales more effectively, thus minimizing the information loss inherent to down-sampling procedures. Additionally, to enhance the convergence rate of network training and mitigate sample imbalance issues, we have devised a modified cross-entropy loss function incorporating a balancing factor. This modification optimizes the distribution between positive and negative samples, thus improving the efficiency of model training. Experimental evaluations of our model demonstrate a substantial reduction in information loss and improved accuracy in semantic segmentation. Notably, our proposed network architecture demonstrates a substantial improvement in the finely annotated mean Intersection over Union (mIoU) on the dataset compared to the conventional SegNet. The proposed network structure not only reduces operational costs by decreasing manual inspection needs but also scales up the deployment of AI-driven image analysis across different sectors.
Joint Association, Beamforming, and Resource Allocation for Multi-IRS Enabled MU-MISO Systems With RSMA
Authors: Chunjie Wang, Xuhui Zhang, Huijun Xing, Liang Xue, Shuqiang Wang, Yanyan Shen, Bo Yang, Xinping Guan
Date: 2024-06-05 00:00:00
Tasks: Scheduling

Intelligent reflecting surface (IRS) and rate-splitting multiple access (RSMA) technologies are at the forefront of enhancing spectrum and energy efficiency in the next generation multi-antenna communication systems. This paper explores a RSMA system with multiple IRSs, and proposes two purpose-driven scheduling schemes, i.e., the exhaustive IRS-aided (EIA) and opportunistic IRS-aided (OIA) schemes. The aim is to optimize the system weighted energy efficiency (EE) under the above two schemes, respectively. Specifically, the Dinkelbach, branch and bound, successive convex approximation, and the semidefinite relaxation methods are exploited within the alternating optimization framework to obtain effective solutions to the considered problems. The numerical findings indicate that the EIA scheme exhibits better performance compared to the OIA scheme in diverse scenarios when considering the weighted EE, and the proposed algorithm demonstrates superior performance in comparison to the baseline algorithms.
Simultaneous Optimized Orthogonal Matching Pursuit with Application to ECG Compression
Authors: Laura Rebollo-Neira
Date: 2024-06-05 00:00:00

A greedy pursuit strategy which finds a common basis for approximating a set of similar signals is proposed. The strategy extends the Optimized Orthogonal Matching Pursuit approach to selecting the subspace containing the approximation of all the signals in the set. The method, called Simultaneous Optimized Orthogonal Matching Pursuit, is stepwise optimal in the sense of minimizing at each iteration the mean error norm of the joint approximation. When applied to compression of electrocardiograms, significant gains over other transformation based compression techniques are demonstrated on the MIT-BIH Arrhythmia dataset.
A Shared-Aperture Dual-Band sub-6 GHz and mmWave Reconfigurable Intelligent Surface With Independent Operation
Authors: Junhui Rao, Yujie Zhang, Shiwen Tang, Zan Li, Zhaoyang Ming, Jichen Zhang, Chi Yuk Chiu, Ross Murch
Date: 2024-06-05 00:00:00
Tasks: Band Gap

A novel dual-band reconfigurable intelligent surface (DBI-RIS) design that combines the functionalities of millimeter-wave (mmWave) and sub-6 GHz bands within a single aperture is proposed. This design aims to bridge the gap between current single-band reconfigurable intelligent surfaces (RISs) and wireless systems utilizing sub-6 GHz and mmWave bands that require RIS with independently reconfigurable dual-band operation. The mmWave element is realized by a double-layer patch antenna loaded with 1-bit phase shifters, providing two reconfigurable states. An 8x8 mmWave element array is selectively interconnected using three RF switches to form a reconfigurable sub-6 GHz element at 3.5 GHz. A suspended electromagnetic band gap (EBG) structure is proposed to suppress surface waves and ensure sufficient geometric space for the phase shifter and control networks in the mmWave element. A low-cost planar spiral inductor (PSI) is carefully optimized to connect mmWave elements, enabling the sub-6 GHz function without affecting mmWave operation. Finally, prototypes of the DBI-RIS are fabricated, and experimental verification is conducted using two separate measurement testbeds. The fabricated sub-6 GHz RIS successfully achieves beam steering within the range of -35 to 35 degrees for DBI-RIS with 4x4 sub-6 GHz elements, while the mmWave RIS demonstrates beam steering between -30 to 30 degrees for DBI-RIS with 8x8 mmWave elements, and have good agreement with simulation results.
Optimization of Rate-Splitting Multiple Access with Integrated Sensing and Backscatter Communication
Authors: Diluka Galappaththige, Shayan Zargari, Chintha Tellambura, Geoffrey Ye Li
Date: 2024-06-04 00:00:00
Tasks: TAG

An integrated sensing and backscatter communication (ISABC) system is introduced herein. This system features a full-duplex (FD) base station (BS) that seamlessly merges sensing with backscatter communication and supports multiple users. Multiple access (MA) for the user is provided by employing rate-splitting multiple access (RSMA). RSMA, unlike other classical orthogonal and non-orthogonal MA schemes, splits messages into common and private streams. With RSMA, the set of common rate forms can be optimized to reduce interference. Optimized formulas are thus derived for communication rates for users, tags, and the BS's sensing rate, with the primary goal of enhancing the transmission efficiency of the BS. The optimization task involves minimizing the BS's overall transmission power by jointly optimizing the BS's beamforming vectors, the tag reflection coefficients, and user common rates. The alternating optimization method is employed to address this challenge. Concrete solutions are provided for the received beamformers, and semi-definite relaxation and slack-optimization techniques are adopted for transmit beamformers and reflection coefficients, respectively. For example, the proposed RSMA-assisted ISABC system achieves a 350% communication rate boost over a nonorthogonal multiple access-assisted ISABC, with only a 24% increase in transmit power, leveraging ten transmit/reception antennas at the BS.
OFDM-Based Active STAR-RIS-Aided Integrated Sensing and Communication Systems
Authors: Hanxiao Ge, Anastasios Papazafeiropoulos, Tharmalingam Ratnarajah
Date: 2024-06-04 00:00:00
Tasks: Integrated sensing and communication, ISAC

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), which consists of numerous passive elements, has recently emerged in wireless communication systems as a promising technology providing 360$^\circ$ coverage and better performance. In our research, we introduce an active STAR-RIS (ASTARS)-aided integrated sensing and communications (ISAC) system designed to optimize the radar signal-to-noise ratio (SNR), enhancing detection and signal transmission efficiency. The introduction of an ISAC system aims to improve both communication efficiency and sensing capabilities. Also, we employ orthogonal frequency division multiplexing (OFDM) to address the frequency-selective fading problem. Furthermore, we evaluate the radar sensing capabilities by examining the range and velocity, and assess the performance through the mean-squared error (MSE) of their estimations. Our simulation results demonstrate that ASTARS outperforms STAR-RIS in our system configurations, and that the proposed optimization approach further enhances the system performance. Additionally, we confirm that an increase in the subcarrier spacing can reduce the transmission bit error rate (BER) under high-velocity conditions.
A DAFT Based Unified Waveform Design Framework for High-Mobility Communications
Authors: Xingyao Zhang, Haoran Yin, Yanqun Tang, Yu Zhou, Yuqing Liu, Jinming Du, Yipeng Ding
Date: 2024-06-04 00:00:00
Tasks: Diversity

With the increasing demand for multi-carrier communication in high-mobility scenarios, it is urgent to design new multi-carrier communication waveforms that can resist large delay-Doppler spreads. Various multi-carrier waveforms in the transform domain were proposed for the fast time-varying channels, including orthogonal time frequency space (OTFS), orthogonal chirp division multiplexing (OCDM), and affine frequency division multiplexing (AFDM). Among these, the AFDM is a strong candidate for its low implementation complexity and ability to achieve optimal diversity. This paper unifies the waveforms based on the discrete affine Fourier transform (DAFT) by using the chirp slope factor "k" in the time-frequency representation to construct a unified design framework for high-mobility communications. The design framework is employed to verify that the bit error rate performance of the DAFT-based waveform can be enhanced when the signal-to-noise ratio (SNR) is sufficiently high by adjusting the chirp slope factor "k".
System Design and Parameter Optimization for Remote Coverage from NOMA-based High-Altitude Platform Stations (HAPS)
Authors: Sidrah Javed, Mohamed-Slim Alouini
Date: 2024-06-04 00:00:00

Stratospheric solar-powered high-altitude platform stations (HAPS) have recently gained immense popularity for their ubiquitous connectivity and resilient operation while providing/catalyzing advanced mobile wireless communication services. They have particularly emerged as promising alternatives for economic coverage of remote areas in the world. This makes them suitable candidates to meet the UN Sustainable Development Goals (SDG-2030) for global connectivity. HAPS can provide line-of-sight (LoS) communications to the ground users in its ultra-wide coverage area. We propose to divide these users into multiple user groups and serve each group with a high-density flexible narrow spot beam, generated by the phased array antennas mounted on HAPS, to achieve high data rates. We carry out the user association and power allocation in a downlink (DL) non-orthogonal multiple access (NOMA) scheme in each user group. To improve the system performance, a sum rate maximization problem is formulated by jointly designing user grouping, user association, beam optimization, and power allocation while guaranteeing the quality-of-service (QoS) for users with limited power budget. We further investigate the outage performance of the users with the proposed approach as compared to the traditional scheme. Our findings reveal the significance of the joint design of communication parameters for enhanced system performance, optimum energy utilization, and resource allocation.
Optimizing Air-borne Network-in-a-box Deployment for Efficient Remote Coverage
Authors: Sidrah Javed, Yunfei Chen, Mohamed-Slim Alouini, Cheng-Xiang Wang
Date: 2024-06-04 00:00:00

Among many envisaged drivers for the sixth generation, one is from the United Nations Sustainability Development Goals 2030 to eliminate digital inequality. Remote coverage in sparsely populated areas, difficult terrains, or emergency scenarios requires on-demand access and flexible deployment with minimal capex and opex. In this context, network-in-a-box (NIB) is an exciting solution that packs the whole wireless network into a single portable and re-configurable box to support multiple access technologies such as WiFi/2G/3G/4G/5G etc. In this paper, we propose low-altitude platform stations (LAPS) based NIBs with stratospheric high-altitude platform station (HAPS) as backhaul. Specifically, backhaul employs non-orthogonal multiple access (NOMA) with superposition coding at the transmitting HAPS and successive interference cancellation (SIC) at the receiving NIBs, whereas the access link (AL) employs superposition coding along with the regularized zero-forcing (RZF) precoding at the NIB in order to elevate the computational overhead from the ground users. The required number of airborne NIBs to serve a desired coverage area, their optimal placement, user association, beam optimization, and resource allocation are optimized by maximizing the sum rate of the AL while maintaining the quality of service. Our findings reveal the significance of thorough system planning and communication parameters optimization for enhanced system performance and best coverage under limited resources.
Pareto Optimal Hybrid Beamforming for Short-Packet Millimeter-Wave Integrated Sensing and Communication
Authors: Jitendra Singh, Banda Naveen, Suraj Srivastava, Aditya K. Jagannatham, Lajos Hanzo
Date: 2024-06-04 00:00:00
Tasks: Integrated sensing and communication, ISAC

Pareto optimal solutions are conceived for radar beamforming error (RBE) and sum rate maximization in short-packet (SP) millimeter-wave (mmWave) integrated sensing and communication (ISAC). Our ultimate goal is to realize ultra-reliable low-latency communication (uRLLC) and real-time sensing capabilities for 6G applications. The ISAC base station (BS) transmits short packets in the downlink (DL) to serve multiple communication users (CUs) and detect multiple radar targets (RTs). We investigate the performance trade-off between the sensing and communication capabilities by optimizing both the radio frequency (RF) and the baseband (BB) transmit precoder (TPC), together with the block lengths. The optimization problem considers the minimum rate requirements of the CUs, the maximum tolerable radar beamforming error (RBE) for the RTs, the unit modulus (UM) elements of the RF TPC, and the finite transmit power as the constraints for SP transmission. The resultant problem is highly non-convex due to the intractable rate expression of the SP regime coupled with the non-convex rate and UM constraints. To solve this problem, we propose an innovative two-layer bisection search (TLBS) algorithm, wherein the RF and BB TPCs are optimized in the inner layer, followed by the block length in the outer layer. Furthermore, a pair of novel methods, namely a bisection search-based majorizer and minimizer (BMM) as well as exact penalty-based manifold optimization (EPMO) are harnessed for optimizing the RF TPC in the inner layer. Subsequently, the BB TPC and the block length are derived via second-order cone programming (SOCP) and mixed integer programming methods, respectively. Finally, our exhaustive simulation results reveal the effect of system parameters for various settings on the RBE-rate region of the SP mmWave ISAC system and demonstrate a significantly enhanced performance compared to the benchmarks.
Cell-free massive MIMO Channels in an Urban Environment -- Measurements and Channel Statistics
Authors: Yuning Zhang, Thomas Choi, Zihang Cheng, Jorge Gomez-Ponce, Issei Kanno, Masaaki Ito, Andreas F. Molisch
Date: 2024-06-03 00:00:00

Cell-free massive MIMO (CF-mMIMO), where each user equipment (UE) is connected to multiple access points (APs), is emerging as an important component for 5G and 6G cellular systems. Accurate channel models based on measurements are required to optimize their design and deployment. This paper presents an extensive measurement campaign for CF-mMIMO in an urban environment. A new "virtual AP" technique measures channels between 80 UE locations and more than 20,000 possible microcellular AP locations. Measurements are done at 3.5 GHz carrier frequency with 350 MHz bandwidth (BW). The paper describes the measurement setup and data processing, shows sample results and their physical interpretation, and provides statistics for key quantities such as pathloss, shadowing, delay spread (DS), and delay window. We find pathloss coefficients of 2.9 and 10.4 for line-of-sight (LOS) and non line-of-sight (NLOS), respectively, where the high LOS coefficient is mainly because larger distance leads to more grazing angle of incidence and thus lower antenna gain in our setup. Shadowing standard deviations are 5.1/16.6 dB, and root mean squared (RMS) DSs of -80.6/-72.6 dBs. The measurements can also be used for parameterizing a CUNEC-type model, which will be reported in future work.
Interbeat Interval Filtering
Authors: Ilker Bayram
Date: 2024-06-03 00:00:00
Tasks: Heart Rate Variability

A number of inhibitory and excitatory factors regulate the beating of the heart. Consequently, the inter-beat intervals (IBIs) are not constant but vary around a mean value, even in the absence of external factors like exercise or stress. Various statistics have been proposed to capture the heart rate variability (HRV) to provide a glimpse into this balance. These statistics usually require accurate estimation of IBIs as a first step. However, estimating IBIs accurately can be challenging in practice, especially for signals recorded in ambulatory conditions. We propose a lightweight state-space filter that models the IBIs as samples of an inverse Gaussian distribution with time-varying parameters. We make the filter robust against outliers by adapting the probabilistic data association filter to the setup. We demonstrate that the resulting filter can accurately identify outliers and the parameters of the tracked distribution can be used to compute a specific HRV statistic (standard deviation of normal-to-normal intervals) without further analysis.
Multi-Task Learning for Arousal and Sleep Stage Detection Using Fully Convolutional Networks
Authors: Hasan Zan, Abdulnasir Yildiz
Date: 2024-06-03 00:00:00
Tasks: EEG, Multi-Task Learning, Sleep Quality, Sleep Stage Detection

Objective. Sleep is a critical physiological process that plays a vital role in maintaining physical and mental health. Accurate detection of arousals and sleep stages is essential for the diagnosis of sleep disorders, as frequent and excessive occurrences of arousals disrupt sleep stage patterns and lead to poor sleep quality, negatively impacting physical and mental health. Polysomnography is a traditional method for arousal and sleep stage detection that is time-consuming and prone to high variability among experts. Approach. In this paper, we propose a novel multi-task learning approach for arousal and sleep stage detection using fully convolutional neural networks. Our model, FullSleepNet, accepts a full-night single-channel EEG signal as input and produces segmentation masks for arousal and sleep stage labels. FullSleepNet comprises four modules: a convolutional module to extract local features, a recurrent module to capture long-range dependencies, an attention mechanism to focus on relevant parts of the input, and a segmentation module to output final predictions. Main results. By unifying the two interrelated tasks as segmentation problems and employing a multi-task learning approach, FullSleepNet achieves state-of-the-art performance for arousal detection with an area under the precision-recall curve of 0.70 on Sleep Heart Health Study and Multi-Ethnic Study of Atherosclerosis datasets. For sleep stage classification, FullSleepNet obtains comparable performance on both datasets, achieving an accuracy of 0.88 on the former and an accuracy of 0.83 on the latter. Significance. Our results demonstrate that FullSleepNet offers improved practicality, efficiency, and accuracy for the detection of arousal and classification of sleep stages using raw EEG signals as input.
Noise And Artifacts Elimination In ECG Signals Using Wavelet, Variational Mode Decomposition And Nonlocal Means Algorithm
Authors: Taoufik Ben Jabeur, Eihab Bashier, Qudsia Sandhu, Kelvin Joseph Bwalya, Adason Joshua
Date: 2024-06-03 00:00:00

Electrocardiogram (ECG) signals can frequently be affected by the introduction of noise and artifacts. Since these types of signal corruptions disrupt the accurate interpretation of ECG signals, noise and artifacts must be eliminated during the preprocessing phase. In this paper, we introduced a comprehensive pre-processing phase that eliminates motion artifacts and noise prior to detecting and extracting entirely corrupted ECG signal segments. The first method, denoted as the WLNH method, is constructed using wavelet multiresolution analysis (MRA), the Lillifors test, NLM, and a high-pass filter. The second method entails substituting the wavelet MRA decomposition with the variational mode decomposition (VMD) while retaining all other stages from the first method. This technique is denoted as the VLWNH. The two proposed methods differ from some existing methods in that they first employ the Lilliefors test to identify whether a component is white Gaussian noise and then utilize the High Pass Filter to eliminate motion anomalies. The simulation results show that the offered solutions are effective, particularly when dealing with white Gaussian noise and base-line wander (BW) noise.
Joint Frame Structure, Beamwidth, and Power Allocation for UAV-Aided Localization and Communication
Authors: Tianhao. Liang, Tingting. Zhang, Sheng. Zhou, Wentao. Liu, Dong. Li, Qinyu. Zhang
Date: 2024-06-03 00:00:00

In wireless sensors networks, integrating localization and communications techniques is crucial for efficient spectrum and hardware utilization. In this paper, we present a novel framework of unmanned aerial vehicle (UAV)-aided localization and communication for ground node (GN), where the average spectral efficiency (SE) is used to reveal the intricate relationship among frame structure, channel estimation error, and localization accuracy. In particular, we first derive the lower bounds for channel estimation error and the three dimensional location prediction error. Leveraging these comprehensive analysis, we formulate a problem to maximize the average SE in UAV-GN communication, where the frame structure, beamwidth and power allocation are jointly optimized. Subsequently, we propose an efficient iterative algorithm to address this non-convex problem with closed-form expressions for beamwidth and power allocation. Numerical results demonstrate that the performance of our proposed method can approach the upper bound with much lower complexity, and achieve over 70\% performance gain compared to non-localization benchmarks. Additionally, the analysis highlights the dominant impacts from the Doppler effect over noise on the average SE.