For: The paper aims to improve the performance of seizure detection systems using EEG signals by learning informative embeddings of the signals.* Methods: The proposed method converts raw EEG signals to appropriate embeddings, which are beneficial for various machine learning models.* Results: The proposed approach achieves significant improvements in sensitivity, specificity, and AUC score across multiple models, with a state-of-the-art classification performance of 100% sensitivity and 99% specificity.Here is the same information in Simplified Chinese text:* For: 这篇论文目的是使用EEG信号提高癫痫检测系统的性能。* Methods: 提议的方法是将原始EEG信号转换为有用的嵌入，这些嵌入对多种机器学习模型都是有利的。* Results: 提议的方法在多个模型上实现了显著提高的敏感性、特异性和AUC分数，并达到了新的顶峰性，即100%的敏感度和99%的特异度。Abstract
Epilepsy is one of the most prevalent brain disorders that disrupts the lives of millions worldwide. For patients with drug-resistant seizures, there exist implantable devices capable of monitoring neural activity, promptly triggering neurostimulation to regulate seizures, or alerting patients of potential episodes. Next-generation seizure detection systems heavily rely on high-accuracy machine learning-based classifiers to detect the seizure onset. Here, we propose to enhance the seizure detection performance by learning informative embeddings of the EEG signal. We empirically demonstrate, for the first time, that converting raw EEG signals to appropriate embeddings can significantly boost the performance of seizure detection algorithms. Importantly, we show that embedding features, which converts the raw EEG into an alternative representation, is beneficial for various machine learning models such as Logistic Regression, Multi-Layer Perceptron, Support Vector Machines, and Gradient Boosted Trees. The experiments were conducted on the CHB-MIT scalp EEG dataset. With the proposed EEG feature embeddings, we achieve significant improvements in sensitivity, specificity, and AUC score across multiple models. By employing this approach alongside an SVM classifier, we were able to attain state-of-the-art classification performance with a sensitivity of 100% and specificity of 99%, setting a new benchmark in the field.
摘要
Translated into Simplified Chinese:epsilepsy 是全球范围内最常见的脑部疾病之一，影响了数百万人。为了治疗这些药物抵抗性的癫痫病人，存在可以监测神经活动，迅速诱发神经刺激来调节癫痫的嵌入式设备。未来的癫痫检测系统几乎完全依赖于高精度机器学习模型来检测癫痫开始。在这里，我们提议通过学习有用的嵌入来增强癫痫检测性能。我们实际地示证，对于第一次癫痫检测，将raw EEG信号转换为合适的嵌入可以显著提高癫痫检测算法的性能。此外，我们还证明了嵌入特征可以为不同的机器学习模型，如Logistic Regression、Multi-Layer Perceptron、Support Vector Machines和Gradient Boosted Trees等提供有利。实验在CHB-MIT皮帽EEG数据集上进行。通过我们的EEG特征嵌入，我们在多个模型上实现了显著的改善，包括敏感性、特异性和AUC分数。通过与SVM分类器结合使用，我们实现了当前领域的最佳分类性能，敏感性为100%，特异性为99%。
Cluster-Based Cell-Free Massive MIMO Systems: A Novel Framework to Enhance Spectral Efficiency with Low Complexity
paper_authors: Reza Roshanghias, Reza Saadat for: This paper aims to improve the spectral efficiency (SE) of distributed cell-free massive MIMO (CF-mMIMO) systems by proposing a novel cluster-based architecture.methods: The proposed cluster-based structure combines centralized and distributed configurations, with local precoders formed using collectively shared CSI within each cluster. The MMSE precoding technique is used to achieve optimal SE performance.results: The simulation results show that the proposed cluster-based framework achieves a significantly augmented SE compared to the distributed architecture, with the optimal SE attained using four clusters and the MMSE precoding technique. The computational complexity is reduced by over 85%. Additionally, the proposed approach outperforms the centralized structure in terms of SE.Here is the text in Simplified Chinese:for: 本文目的是提高分布式cell-free大MIMO系统的spectral efficiency（SE）。methods: 提议的集群结构 combinest中央化和分布式配置，通过每个集群内CSI的共享来形成本地预编码器。使用MMSE预编码技术来实现优化的SE性能。results: 仪表结果表明，提议的集群结构相比分布式结构，可以获得显著提高的SE性能，最佳SE性能在四个集群和MMSE预编码技术下实现，计算复杂度下降超过85%。此外，提议的方法还超越了中央结构的SE性能。Abstract
The issue of diminished spectral efficiency (SE) of the downlink (DL) transmission in distributed cell-free massive MIMO (CF-mMIMO) systems poses a significant challenge in terms of user equipment (UE) performance when compared to their centralized CF-mMIMO counterparts. The primary root cause of this issue can be attributed to the reduced efficacy of distributed precoders, which are devised using local channel state information (CSI) in distributed systems. This reduced efficacy becomes particularly pronounced in terms of interference mitigation when compared to centralized precoders. To address this issue, this paper proposes a novel architectural framework for CF-mMIMO systems, referred to herein as the "cluster-based structure." Within this innovative structure, a hybrid amalgamation of centralized and distributed configurations is employed, complemented by the introduction of a unique cluster arrangement for the access points (APs) within the network. In this design, the CSI of APs within each cluster is collectively shared within a local processor unit. Consequently, by harnessing this enhanced repository of local channel information, local precoders are formulated, which facilitate more effective interference mitigation with reduced computational complexity compared to the centralized approach. This approach ultimately results in a significantly augmented SE when contrasted with the distributed architecture. The simulation results unequivocally demonstrate that within the cluster-based framework, the optimal SE for the network is attained when utilizing four clusters in conjunction with the MMSE precoding technique, leading to a notable reduction in computational complexity exceeding 85%. Importantly, this approach surpasses the SE performance of the centralized structure.
摘要
分布式Cell-free巨观MIMO系统（CF-mMIMO）的下行传输（DL） Spectral Efficiency（SE）受到了明显的挑战，用户设备（UE）性能与中央CF-mMIMO对照之下显著下降。主要的根本原因在于分布式预编器的效果减退，这些预编器基于分布式系统中的本地频道状态信息（CSI）设计。在分布式系统中，这种减退的效果特别明显在干扰抑制方面，与中央预编器相比。为解决这一问题，本文提出了一种新的建筑框架，称为“分区结构”。在这种新的架构中， hybrid化中央和分布式配置是使用，并在网络中的APs之间创建了特有的帧排序。在这个设计中，APs在每个分区内的CSI是集中共享在本地处理单元中。通过利用这些增强的本地频道信息，本地预编器是计算更有效的干扰抑制，比中央方法更加简单。这种方法最终导致了分布式系统中的SE显著增加，与分布式架构相比，SE性能得到了显著提高。实验结果表明，在分区结构中，使用四个分区并与MMSE预编器技术相结合，可以获得网络的最佳SE，计算复杂度超过85%。这种方法还超越了中央结构的SE性能。
Two-stage space construction for real-time modeling of distributed parameter systems under sparse sensing
for: This paper is written for real-time modeling of distributed parameter systems (DPSs) in cases of limited sensors.
methods: The paper introduces a two-stage spatial construction approach that uses a discrete space-completion method to recuperate spatiotemporal patterns of non-monitored locations, followed by the use of high-dimensional space construction methods to derive continuous spatial basis functions (SBFs). The nonlinear temporal model is identified and adjusted via long short-term memory (LSTM) neural networks.
results: The paper demonstrates the efficacy of the proposed modeling technique under sparse sensing using experimental tests conducted on a pouch-type Li-ion battery. The results show that the use of a cubic B-spline surface is an effective solution for optimizing space construction in the sense of least squares approximation.Abstract
Numerous industrial processes can be defined using distributed parameter systems (DPSs). This study introduces a two-stage spatial construction approach for real-time modeling of DPSs in cases of limited sensors. Initially, a discrete space-completion approach is created to recuperate the spatiotemporal patterns of non-monitored locations under sparse sensing. The high-dimensional space construction method is employed to derive continuous spatial basis functions (SBFs). The identification and adjustment of the nonlinear temporal model are carried out via the long short-term memory (LSTM) neural network. Eventually, the amalgamation of the derived SBFs and temporal model results in a spatially continuous model. The use of a cubic B-spline surface is validated as an effective solution for optimizing space construction in the sense of least squares approximation. Experimental tests conducted on a pouch-type Li-ion battery demonstrate the efficacy of the proposed modeling technique under sparse sensing. This work highlights the promise of sparse sensors in real-time full-space modeling for large-scale battery energy storage systems.
摘要
许多工业过程可以使用分布参数系统（DPS）进行定义。本研究提出了一种两Stage空间建构方法，用于实时模拟DPS，并且在有限感知的情况下进行模拟。首先，一种精简空间完成方法被创建，以恢复不监测区域的空间时间模式。然后，高维空间建构方法被应用，以 derivate连续空间基函数（SBF）。非线性时间模型的标识和调整由长Short-Term记忆神经网络（LSTM）完成。最后， derivate的 SBFs 和时间模型的结合，得到了一个连续空间模型。实验表明，使用立方BSpline面的方法可以有效地优化空间建构，从 least squares 的角度来看。这种方法在磁力牵引Li-ion电池的实验中得到了证明，并且表明了有限感知的感知器在实时全空间模型化方面的承诺。
Joint Localization and Communication Enhancement in Uplink Integrated Sensing and Communications System with Clock Asynchronism
results: 可以减少时钟偏移（TO）相关的频率变化引起的影响，并且可以实现单基站的射频定位。在实验中，提出的方案可以与最小二乘均方差（MMSE）CSI估计具有相同的比特错误率性能，并且可以提高射频定位的均方差Error（MSE）约8分质量单位。Abstract
In this paper, we propose a joint single-base localization and communication enhancement scheme for the uplink (UL) integrated sensing and communications (ISAC) system with asynchronism, which can achieve accurate single-base localization of user equipment (UE) and significantly improve the communication reliability despite the existence of timing offset (TO) due to the clock asynchronism between UE and base station (BS). Our proposed scheme integrates the CSI enhancement into the multiple signal classification (MUSIC)-based AoA estimation and thus imposes no extra complexity on the ISAC system. We further exploit a MUSIC-based range estimation method and prove that it can suppress the time-varying TO-related phase terms. Exploiting the AoA and range estimation of UE, we can estimate the location of UE. Finally, we propose a joint CSI and data signals-based localization scheme that can coherently exploit the data and the CSI signals to improve the AoA and range estimation, which further enhances the single-base localization of UE. The extensive simulation results show that the enhanced CSI can achieve equivalent bit error rate performance to the minimum mean square error (MMSE) CSI estimator. The proposed joint CSI and data signals-based localization scheme can achieve decimeter-level localization accuracy despite the existing clock asynchronism and improve the localization mean square error (MSE) by about 8 dB compared with the maximum likelihood (ML)-based benchmark method.
摘要
在这篇论文中，我们提出了一种同时进行单基地位置定位和通信增强方案，用于下降链（UL）结合感知通信（ISAC）系统中的异步问题，可以实现精确的单基地位置定位和提高通信可靠性，即使存在时钟偏移（TO）。我们的提议方案将增强因子（CSI）增强纳入多个信号分类（MUSIC）基于投射角（AoA）估计中，从而不增加ISAC系统的复杂性。我们进一步利用MUSIC基于距离估计方法，并证明它可以抑制时间变化的TO相关阶跃项。通过UE的AoA和距离估计，我们可以估计UE的位置。最后，我们提议一种同时使用CSI和数据信号的位置定位方案，可以具有协调利用数据和CSI信号来提高AoA和距离估计的优点，进而提高单基地位置定位精度。实验结果表明，提高CSI可以实现与最小平均方差（MMSE）CSI估计器相同的错误率性能。我们的联合CSI和数据信号基于位置定位方案可以在存在时钟偏移的情况下实现厘米级位置定位精度，并提高位置估计均方差（MSE）约8分贝比对最大likelihood（ML）参考方法。
A Generalized Statistical Model for THz wireless Channel with Random Atmospheric Absorption
results: 论文通过fox的H函数来描述信道障碍的共同统计效应，并分析了THz链路的失业概率，以证明提出的通用模型的分析可行性。 computer simulations也用于证明该模型在性能评估中的效果。Abstract
Current statistical channel models for Terahertz (THz) wireless communication primarily concentrate on the sub-THz band, mostly with $\alpha$-$\mu$ and Gaussian mixture fading distributions for short-term fading and deterministic modeling for atmospheric absorption. In this paper, we develop a generalized statistical model for signal propagation at THz frequencies considering random path-loss employing Gamma distribution for the molecular absorption coefficient, short-term fading characterized by the $\alpha$-$\eta$-$\kappa$-$\mu$ distribution, antenna misalignment errors, and transceiver hardware impairments. The proposed model can handle various propagation scenarios, including indoor and outdoor environments, backhaul/fronthaul situations, and complex urban settings. Using Fox's H-functions, we present the probability density function (PDF) and cumulative distribution function (CDF) that capture the combined statistical effects of channel impairments. We analyze the outage probability of a THz link to demonstrate the analytical tractability of the proposed generalized model. We present computer simulations to demonstrate the efficacy of the proposed model for performance assessment with the statistical effect of atmospheric absorption.
摘要
当前的天 Harrison（THz）无线通信频率模型主要集中在Sub-THz频段，通常使用α-μ和高斯混合折射分布来描述短期折射和大气吸收的 deterministic 模型。在本文中，我们开发了一种通用的天 Harrison（THz）信号卫星传播模型，考虑了随机路径损失，使用γ分布来描述分子吸收系数，短期折射由α-η-κ-μ分布 characterize，天线误差、发射机硬件不良等因素。该模型可以处理不同的传播enario，包括室内和室外环境，后向/前向 Situation，复杂的城市场景。使用Fox的H函数，我们提供了PDF和CDF，这些函数捕捉了天 Harrison（THz）通信频率的共同统计效应。我们分析了THz链接的失业概率，以示analytical tractability of the proposed generalized model。我们通过计算机实验证明了提案的模型在性能评估中的有效性，并且演示了天 Harrison（THz）通信频率的统计效应。