Deep learning methods have been widely used as an end-to-end modeling strategy of electrical energy systems because of their conveniency and powerful pattern recognition capability. However, due to the "black-box" nature, deep learning methods have long been blamed for their poor interpretability when modeling a physical system. In this paper, we introduce a novel neural network structure, Kolmogorov-Arnold Network (KAN), to achieve "white-box" modeling for electrical energy systems to enhance the interpretability. The most distinct feature of KAN lies in the learnable activation function together with the sparse training and symbolification process. Consequently, KAN can express the physical process with concise and explicit mathematical formulas while remaining the nonlinear-fitting capability of deep neural networks. Simulation results based on three electrical energy systems demonstrate the effectiveness of KAN in the aspects of interpretability, accuracy, robustness and generalization ability.
深度学习方法因其便捷性和强大的模式识别能力，已被广泛用作电能系统的端到端建模策略。然而，由于其 "黑箱 "特性，深度学习方法在物理系统建模时一直被指责为可解释性差。在本文中，我们引入了一种新型的神经网络结构--Kolmogorov-Arnold 网络（KAN），以实现对电能系统的 "白箱 "建模，从而提高可解释性。KAN 的最大特点在于其可学习的激活函数以及稀疏的训练和符号化过程。因此，KAN 可以用简洁明了的数学公式表达物理过程，同时保持深度神经网络的非线性拟合能力。基于三个电能系统的仿真结果证明了 KAN 在可解释性、准确性、鲁棒性和泛化能力等方面的有效性。
{"title":"A White-Box Deep-Learning Method for Electrical Energy System Modeling Based on Kolmogorov-Arnold Network","authors":"Zhenghao Zhou, Yiyan Li, Zelin Guo, Zheng Yan, Mo-Yuen Chow","doi":"arxiv-2409.08044","DOIUrl":"https://doi.org/arxiv-2409.08044","url":null,"abstract":"Deep learning methods have been widely used as an end-to-end modeling\u0000strategy of electrical energy systems because of their conveniency and powerful\u0000pattern recognition capability. However, due to the \"black-box\" nature, deep\u0000learning methods have long been blamed for their poor interpretability when\u0000modeling a physical system. In this paper, we introduce a novel neural network\u0000structure, Kolmogorov-Arnold Network (KAN), to achieve \"white-box\" modeling for\u0000electrical energy systems to enhance the interpretability. The most distinct\u0000feature of KAN lies in the learnable activation function together with the\u0000sparse training and symbolification process. Consequently, KAN can express the\u0000physical process with concise and explicit mathematical formulas while\u0000remaining the nonlinear-fitting capability of deep neural networks. Simulation\u0000results based on three electrical energy systems demonstrate the effectiveness\u0000of KAN in the aspects of interpretability, accuracy, robustness and\u0000generalization ability.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shuai S. A. Yuan, Li Wei, Xiaoming Chen, Chongwen Huang, Wei E. I. Sha
Holographic multiple-input and multiple-output (MIMO) communications introduce innovative antenna array configurations, such as dense arrays and volumetric arrays, which offer notable advantages over conventional planar arrays with half-wavelength element spacing. However, accurately assessing the performance of these new holographic MIMO systems necessitates careful consideration of channel matrix normalization, as it is influenced by array gain, which, in turn, depends on the array topology. Traditional normalization methods may be insufficient for assessing these advanced array topologies, potentially resulting in misleading or inaccurate evaluations. In this study, we propose electromagnetic normalization approaches for the channel matrix that accommodate arbitrary array topologies, drawing on the array gains from analytical, physical, and full-wave methods. Additionally, we introduce a normalization method for near-field MIMO channels based on a rigorous dyadic Green's function approach, which accounts for potential losses of gain at near field. Finally, we perform capacity analyses under quasi-static, ergodic, and near-field conditions, through adopting the proposed normalization techniques. Our findings indicate that channel matrix normalization should reflect the realized gains of the antenna array in target directions. Failing to accurately normalize the channel matrix can result in errors when evaluating the performance limits and benefits of unconventional holographic array topologies, potentially compromising the optimal design of holographic MIMO systems.
全息多输入多输出（MIMO）通信引入了创新的天线阵列配置，如密集阵列和体积阵列，与具有半波长元件间距的传统平面阵列相比具有显著优势。然而，要准确评估这些新型全息多输入多输出系统的性能，就必须仔细考虑信道矩阵归一化问题，因为它受到阵列增益的影响，而阵列增益又取决于阵列拓扑结构。传统的归一化方法可能不足以评估这些先进的阵列拓扑结构，可能导致误导或不准确的评估。在本研究中，我们利用分析、物理和全波方法中的阵列增益，提出了适应任意阵列拓扑的信道矩阵电磁归一化方法。此外，我们还基于严格的二元格林函数方法，为近场 MIMO 信道引入了归一化方法，该方法考虑了近场增益的潜在损失。最后，通过采用所提出的归一化技术，我们对准静态、遍历和近场条件下的容量进行了分析。在评估非常规全息阵列拓扑的性能极限和优势时，如果不能准确归一化信道矩阵，就会导致错误，从而可能影响全息多输入多输出系统的优化设计。
{"title":"Electromagnetic Normalization of Channel Matrix for Holographic MIMO Communications","authors":"Shuai S. A. Yuan, Li Wei, Xiaoming Chen, Chongwen Huang, Wei E. I. Sha","doi":"arxiv-2409.08080","DOIUrl":"https://doi.org/arxiv-2409.08080","url":null,"abstract":"Holographic multiple-input and multiple-output (MIMO) communications\u0000introduce innovative antenna array configurations, such as dense arrays and\u0000volumetric arrays, which offer notable advantages over conventional planar\u0000arrays with half-wavelength element spacing. However, accurately assessing the\u0000performance of these new holographic MIMO systems necessitates careful\u0000consideration of channel matrix normalization, as it is influenced by array\u0000gain, which, in turn, depends on the array topology. Traditional normalization\u0000methods may be insufficient for assessing these advanced array topologies,\u0000potentially resulting in misleading or inaccurate evaluations. In this study,\u0000we propose electromagnetic normalization approaches for the channel matrix that\u0000accommodate arbitrary array topologies, drawing on the array gains from\u0000analytical, physical, and full-wave methods. Additionally, we introduce a\u0000normalization method for near-field MIMO channels based on a rigorous dyadic\u0000Green's function approach, which accounts for potential losses of gain at near\u0000field. Finally, we perform capacity analyses under quasi-static, ergodic, and\u0000near-field conditions, through adopting the proposed normalization techniques.\u0000Our findings indicate that channel matrix normalization should reflect the\u0000realized gains of the antenna array in target directions. Failing to accurately\u0000normalize the channel matrix can result in errors when evaluating the\u0000performance limits and benefits of unconventional holographic array topologies,\u0000potentially compromising the optimal design of holographic MIMO systems.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the extreme scaling, active devices in both CMOS and BiCMOS technologies have reached outstanding ft/fmax, enabling an ever-increasing number of existing and emerging applications in the microwave and millimeter wave (mm-wave) frequency range. The increase in transistors ft/fmax has been so much significant that the performance of microwave and mm-wave ICs are limited mainly by losses in passive devices. In this paper, we address a discussion on qualitative and quantitative aspects that may help to further unveil the impact of such losses on the overall circuit performance and stimulate the adoption of effective loss-aware design methodologies. As example, we report the results related to the design of low power mm-wave low noise amplifiers (LNAs). Our results show how, in low power regime, the performances of mm-wave LNAs are dominated by losses in passive components. We also show how loss-aware design methodologies can mitigate the performance degradation due to passives, resulting as an important tool to get the full potential out of the active devices available today.
{"title":"Millimeter-Wave Integrated Silicon Devices: Active versus Passive -- The Eternal Struggle Between Good and Evil","authors":"Michele Spasaro, Domenico Zito","doi":"arxiv-2409.08176","DOIUrl":"https://doi.org/arxiv-2409.08176","url":null,"abstract":"With the extreme scaling, active devices in both CMOS and BiCMOS technologies\u0000have reached outstanding ft/fmax, enabling an ever-increasing number of\u0000existing and emerging applications in the microwave and millimeter wave\u0000(mm-wave) frequency range. The increase in transistors ft/fmax has been so much\u0000significant that the performance of microwave and mm-wave ICs are limited\u0000mainly by losses in passive devices. In this paper, we address a discussion on\u0000qualitative and quantitative aspects that may help to further unveil the impact\u0000of such losses on the overall circuit performance and stimulate the adoption of\u0000effective loss-aware design methodologies. As example, we report the results\u0000related to the design of low power mm-wave low noise amplifiers (LNAs). Our\u0000results show how, in low power regime, the performances of mm-wave LNAs are\u0000dominated by losses in passive components. We also show how loss-aware design\u0000methodologies can mitigate the performance degradation due to passives,\u0000resulting as an important tool to get the full potential out of the active\u0000devices available today.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anita Beigzadeh, Vahid Yazdnian, Kamaledin Setarehdan
Any person in his/her daily life activities experiences different kinds and various amounts of mental stress which has a destructive effect on their performance. Therefore, it is crucial to come up with a systematic way of stress management and performance enhancement. This paper presents a comprehensive portable and real-time biofeedback system that aims at boosting stress management and consequently performance enhancement. For this purpose, a real-time brain signal acquisition device, a wireless vibration biofeedback device, and a software-defined program for stress level classification have been developed. More importantly, the entire system has been designed to present minimum time delay by propitiously bridging all the essential parts of the system together. We have presented different signal processing and feature extraction techniques for an online stress detection application. Accordingly, by testing the stress classification section of the system, an accuracy of 83% and a recall detecting the true mental stress level of 92% was achieved. Moreover, the biofeedback system as integrity has been tested on 20 participants in the controlled experimental setup. Experiment evaluations show promising results of system performances, and the findings reveal that our system is able to help the participants reduce their stress level by 55% and increase their accuracy by 24.5%. It can be concluded from the observations that all primary premises on stress management and performance enhancement through reward learning are valid as well.
{"title":"Mental Stress Detection and Performance Enhancement Using FNIRS and Wrist Vibrator Biofeedback","authors":"Anita Beigzadeh, Vahid Yazdnian, Kamaledin Setarehdan","doi":"arxiv-2409.08089","DOIUrl":"https://doi.org/arxiv-2409.08089","url":null,"abstract":"Any person in his/her daily life activities experiences different kinds and\u0000various amounts of mental stress which has a destructive effect on their\u0000performance. Therefore, it is crucial to come up with a systematic way of\u0000stress management and performance enhancement. This paper presents a\u0000comprehensive portable and real-time biofeedback system that aims at boosting\u0000stress management and consequently performance enhancement. For this purpose, a\u0000real-time brain signal acquisition device, a wireless vibration biofeedback\u0000device, and a software-defined program for stress level classification have\u0000been developed. More importantly, the entire system has been designed to\u0000present minimum time delay by propitiously bridging all the essential parts of\u0000the system together. We have presented different signal processing and feature\u0000extraction techniques for an online stress detection application. Accordingly,\u0000by testing the stress classification section of the system, an accuracy of 83%\u0000and a recall detecting the true mental stress level of 92% was achieved.\u0000Moreover, the biofeedback system as integrity has been tested on 20\u0000participants in the controlled experimental setup. Experiment evaluations show\u0000promising results of system performances, and the findings reveal that our\u0000system is able to help the participants reduce their stress level by 55% and\u0000increase their accuracy by 24.5%. It can be concluded from the observations\u0000that all primary premises on stress management and performance enhancement\u0000through reward learning are valid as well.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph has no cycles of odd number of negative edges. Laplacian of a balanced signed graph has eigenvectors that map simply to ones in a similarity-transformed positive graph Laplacian, thus enabling reuse of well-studied spectral filters designed for positive graphs. We propose a fast method to learn a balanced signed graph Laplacian directly from data. Specifically, for each node $i$, to determine its polarity $beta_i in {-1,1}$ and edge weights ${w_{i,j}}_{j=1}^N$, we extend a sparse inverse covariance formulation based on linear programming (LP) called CLIME, by adding linear constraints to enforce ``consistent" signs of edge weights ${w_{i,j}}_{j=1}^N$ with the polarities of connected nodes -- i.e., positive/negative edges connect nodes of same/opposing polarities. For each LP, we adapt projections on convex set (POCS) to determine a suitable CLIME parameter $rho > 0$ that guarantees LP feasibility. We solve the resulting LP via an off-the-shelf LP solver in $mathcal{O}(N^{2.055})$. Experiments on synthetic and real-world datasets show that our balanced graph learning method outperforms competing methods and enables the use of spectral filters and graph convolutional networks (GCNs) designed for positive graphs on signed graphs.
{"title":"Efficient Learning of Balanced Signed Graphs via Iterative Linear Programming","authors":"Haruki Yokota, Hiroshi Higashi, Yuichi Tanaka, Gene Cheung","doi":"arxiv-2409.07794","DOIUrl":"https://doi.org/arxiv-2409.07794","url":null,"abstract":"Signed graphs are equipped with both positive and negative edge weights,\u0000encoding pairwise correlations as well as anti-correlations in data. A balanced\u0000signed graph has no cycles of odd number of negative edges. Laplacian of a\u0000balanced signed graph has eigenvectors that map simply to ones in a\u0000similarity-transformed positive graph Laplacian, thus enabling reuse of\u0000well-studied spectral filters designed for positive graphs. We propose a fast\u0000method to learn a balanced signed graph Laplacian directly from data.\u0000Specifically, for each node $i$, to determine its polarity $beta_i in\u0000{-1,1}$ and edge weights ${w_{i,j}}_{j=1}^N$, we extend a sparse inverse\u0000covariance formulation based on linear programming (LP) called CLIME, by adding\u0000linear constraints to enforce ``consistent\" signs of edge weights\u0000${w_{i,j}}_{j=1}^N$ with the polarities of connected nodes -- i.e.,\u0000positive/negative edges connect nodes of same/opposing polarities. For each LP,\u0000we adapt projections on convex set (POCS) to determine a suitable CLIME\u0000parameter $rho > 0$ that guarantees LP feasibility. We solve the resulting LP\u0000via an off-the-shelf LP solver in $mathcal{O}(N^{2.055})$. Experiments on\u0000synthetic and real-world datasets show that our balanced graph learning method\u0000outperforms competing methods and enables the use of spectral filters and graph\u0000convolutional networks (GCNs) designed for positive graphs on signed graphs.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Meiyi Zhu, Matteo Zecchin, Sangwoo Park, Caili Guo, Chunyan Feng, Petar Popovski, Osvaldo Simeone
This paper presents communication-constrained distributed conformal risk control (CD-CRC) framework, a novel decision-making framework for sensor networks under communication constraints. Targeting multi-label classification problems, such as segmentation, CD-CRC dynamically adjusts local and global thresholds used to identify significant labels with the goal of ensuring a target false negative rate (FNR), while adhering to communication capacity limits. CD-CRC builds on online exponentiated gradient descent to estimate the relative quality of the observations of different sensors, and on online conformal risk control (CRC) as a mechanism to control local and global thresholds. CD-CRC is proved to offer deterministic worst-case performance guarantees in terms of FNR and communication overhead, while the regret performance in terms of false positive rate (FPR) is characterized as a function of the key hyperparameters. Simulation results highlight the effectiveness of CD-CRC, particularly in communication resource-constrained environments, making it a valuable tool for enhancing the performance and reliability of distributed sensor networks.
{"title":"Conformal Distributed Remote Inference in Sensor Networks Under Reliability and Communication Constraints","authors":"Meiyi Zhu, Matteo Zecchin, Sangwoo Park, Caili Guo, Chunyan Feng, Petar Popovski, Osvaldo Simeone","doi":"arxiv-2409.07902","DOIUrl":"https://doi.org/arxiv-2409.07902","url":null,"abstract":"This paper presents communication-constrained distributed conformal risk\u0000control (CD-CRC) framework, a novel decision-making framework for sensor\u0000networks under communication constraints. Targeting multi-label classification\u0000problems, such as segmentation, CD-CRC dynamically adjusts local and global\u0000thresholds used to identify significant labels with the goal of ensuring a\u0000target false negative rate (FNR), while adhering to communication capacity\u0000limits. CD-CRC builds on online exponentiated gradient descent to estimate the\u0000relative quality of the observations of different sensors, and on online\u0000conformal risk control (CRC) as a mechanism to control local and global\u0000thresholds. CD-CRC is proved to offer deterministic worst-case performance\u0000guarantees in terms of FNR and communication overhead, while the regret\u0000performance in terms of false positive rate (FPR) is characterized as a\u0000function of the key hyperparameters. Simulation results highlight the\u0000effectiveness of CD-CRC, particularly in communication resource-constrained\u0000environments, making it a valuable tool for enhancing the performance and\u0000reliability of distributed sensor networks.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xianghao Zhan, Zhou Zhou, Yuzhe Liu, Nicholas J. Cecchi, Marzieh Hajiahamemar, Michael M. Zeineh, Gerald A. Grant, David Camarillo, Svein Kleiven
Brain deformation caused by a head impact leads to traumatic brain injury (TBI). The maximum principal strain (MPS) was used to measure the extent of brain deformation and predict injury, and the recent evidence has indicated that incorporating the maximum principal strain rate (MPSR) and the product of MPS and MPSR, denoted as MPSxSR, enhances the accuracy of TBI prediction. However, ambiguities have arisen about the calculation of MPSR. Two schemes have been utilized: one (MPSR1) is to use the time derivative of MPS, and another (MPSR2) is to use the first eigenvalue of the strain rate tensor. Both MPSR1 and MPSR2 have been applied in previous studies to predict TBI. To quantify the discrepancies between these two methodologies, we conducted a comparison of these two MPSR methodologies across nine in-vivo and in-silico head impact datasets and found that 95MPSR1 was 5.87% larger than 95MPSR2, and 95MPSxSR1 was 2.55% larger than 95MPSxSR2. Across every element in all head impacts, MPSR1 was 8.28% smaller than MPSR2, and MPSxSR1 was 8.11% smaller than MPSxSR2. Furthermore, logistic regression models were trained to predict TBI based on the MPSR (or MPSxSR), and no significant difference was observed in the predictability across different variables. The consequence of misuse of MPSR and MPSxSR thresholds (i.e. compare threshold of 95MPSR1 with value from 95MPSR2 to determine if the impact is injurious) was investigated, and the resulting false rates were found to be around 1%. The evidence suggested that these two methodologies were not significantly different in detecting TBI.
{"title":"Differences between Two Maximal Principal Strain Rate Calculation Schemes in Traumatic Brain Analysis with in-vivo and in-silico Datasets","authors":"Xianghao Zhan, Zhou Zhou, Yuzhe Liu, Nicholas J. Cecchi, Marzieh Hajiahamemar, Michael M. Zeineh, Gerald A. Grant, David Camarillo, Svein Kleiven","doi":"arxiv-2409.08164","DOIUrl":"https://doi.org/arxiv-2409.08164","url":null,"abstract":"Brain deformation caused by a head impact leads to traumatic brain injury\u0000(TBI). The maximum principal strain (MPS) was used to measure the extent of\u0000brain deformation and predict injury, and the recent evidence has indicated\u0000that incorporating the maximum principal strain rate (MPSR) and the product of\u0000MPS and MPSR, denoted as MPSxSR, enhances the accuracy of TBI prediction.\u0000However, ambiguities have arisen about the calculation of MPSR. Two schemes\u0000have been utilized: one (MPSR1) is to use the time derivative of MPS, and\u0000another (MPSR2) is to use the first eigenvalue of the strain rate tensor. Both\u0000MPSR1 and MPSR2 have been applied in previous studies to predict TBI. To\u0000quantify the discrepancies between these two methodologies, we conducted a\u0000comparison of these two MPSR methodologies across nine in-vivo and in-silico\u0000head impact datasets and found that 95MPSR1 was 5.87% larger than 95MPSR2, and\u000095MPSxSR1 was 2.55% larger than 95MPSxSR2. Across every element in all head\u0000impacts, MPSR1 was 8.28% smaller than MPSR2, and MPSxSR1 was 8.11% smaller than\u0000MPSxSR2. Furthermore, logistic regression models were trained to predict TBI\u0000based on the MPSR (or MPSxSR), and no significant difference was observed in\u0000the predictability across different variables. The consequence of misuse of\u0000MPSR and MPSxSR thresholds (i.e. compare threshold of 95MPSR1 with value from\u000095MPSR2 to determine if the impact is injurious) was investigated, and the\u0000resulting false rates were found to be around 1%. The evidence suggested that\u0000these two methodologies were not significantly different in detecting TBI.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Van-Phuc Bui, Pedro Maia de Sant Ana, Soheil Gherekhloo, Shashi Raj Pandey, Petar Popovski
This paper presents a Digital Twin (DT) framework for the remote control of an Autonomous Guided Vehicle (AGV) within a Network Control System (NCS). The AGV is monitored and controlled using Integrated Sensing and Communications (ISAC). In order to meet the real-time requirements, the DT computes the control signals and dynamically allocates resources for sensing and communication. A Reinforcement Learning (RL) algorithm is derived to learn and provide suitable actions while adjusting for the uncertainty in the AGV's position. We present closed-form expressions for the achievable communication rate and the Cramer-Rao bound (CRB) to determine the required number of Orthogonal Frequency-Division Multiplexing (OFDM) subcarriers, meeting the needs of both sensing and communication. The proposed algorithm is validated through a millimeter-Wave (mmWave) simulation, demonstrating significant improvements in both control precision and communication efficiency.
{"title":"Digital Twin for Autonomous Guided Vehicles based on Integrated Sensing and Communications","authors":"Van-Phuc Bui, Pedro Maia de Sant Ana, Soheil Gherekhloo, Shashi Raj Pandey, Petar Popovski","doi":"arxiv-2409.08005","DOIUrl":"https://doi.org/arxiv-2409.08005","url":null,"abstract":"This paper presents a Digital Twin (DT) framework for the remote control of\u0000an Autonomous Guided Vehicle (AGV) within a Network Control System (NCS). The\u0000AGV is monitored and controlled using Integrated Sensing and Communications\u0000(ISAC). In order to meet the real-time requirements, the DT computes the\u0000control signals and dynamically allocates resources for sensing and\u0000communication. A Reinforcement Learning (RL) algorithm is derived to learn and\u0000provide suitable actions while adjusting for the uncertainty in the AGV's\u0000position. We present closed-form expressions for the achievable communication\u0000rate and the Cramer-Rao bound (CRB) to determine the required number of\u0000Orthogonal Frequency-Division Multiplexing (OFDM) subcarriers, meeting the\u0000needs of both sensing and communication. The proposed algorithm is validated\u0000through a millimeter-Wave (mmWave) simulation, demonstrating significant\u0000improvements in both control precision and communication efficiency.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, an algorithm for Quantum Inverse Fast Fourier Transform (QIFFT) is developed to work for quantum data. Analogous to a classical discrete signal, a quantum signal can be represented in Dirac notation, application of QIFFT is a tensor transformation from frequency domain to time domain. If the tensors are merely complex entries, then we get the classical scenario. We have included the complete formulation of QIFFT algorithm from the classical model and have included butterfly diagram. QIFFT outperforms regular inversion of Quantum Fourier Transform (QFT) in terms of computational complexity, quantum parallelism and improved versatility.
{"title":"Quantum Inverse Fast Fourier Transform","authors":"Mayank Roy, Devi Maheswaran","doi":"arxiv-2409.07983","DOIUrl":"https://doi.org/arxiv-2409.07983","url":null,"abstract":"In this paper, an algorithm for Quantum Inverse Fast Fourier Transform\u0000(QIFFT) is developed to work for quantum data. Analogous to a classical\u0000discrete signal, a quantum signal can be represented in Dirac notation,\u0000application of QIFFT is a tensor transformation from frequency domain to time\u0000domain. If the tensors are merely complex entries, then we get the classical\u0000scenario. We have included the complete formulation of QIFFT algorithm from the\u0000classical model and have included butterfly diagram. QIFFT outperforms regular\u0000inversion of Quantum Fourier Transform (QFT) in terms of computational\u0000complexity, quantum parallelism and improved versatility.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose a technique of signal acquisition using a combination of two devices with different sampling rates and quantization accuracies. Subsequent processing involving sparsity regularization enables us to reconstruct the signal at such a sampling frequency and with such a bit depth that was not possible using the two devices independently. Objective and subjective tests show the superiority of the proposed method in comparison with alternatives.
{"title":"Dequantization of a signal from two parallel quantized observations","authors":"Vojtěch Kovanda, Pavel Rajmic","doi":"arxiv-2409.08071","DOIUrl":"https://doi.org/arxiv-2409.08071","url":null,"abstract":"We propose a technique of signal acquisition using a combination of two\u0000devices with different sampling rates and quantization accuracies. Subsequent\u0000processing involving sparsity regularization enables us to reconstruct the\u0000signal at such a sampling frequency and with such a bit depth that was not\u0000possible using the two devices independently. Objective and subjective tests\u0000show the superiority of the proposed method in comparison with alternatives.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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