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25th International ITG Workshop on Smart Antennas, WSA 2021

作者： Zhao, Hui Lampiris, Eleftherios Caire, Giuseppe Elia, Petros Communication Systems Department EURECOM Sophia Antipolis 06410 France Electrical Engineering and Computer Science Department Technical University of Berlin Berlin10587 Germany

ISBN: (纸本)9783800756889

We investigate the performance of multi-antenna coded caching delivery algorithms operating under practical constraints. Specifically, under the constraints of finite subpacketization and finite signal-to-noise ratio (SNR) we compare two popular algorithms, one exhibiting low subpacketization while the other transmits multicast messages, hence requiring fewer symbols for communication and resulting in increased power per transmitted symbol. Our work is motivated by the fact that, while many multi-antenna coded caching algorithms exhibit the same asymptotic performance, when factoring practical limitations such as finite subpacketization and finite SNR, the real-world performances differ significantly. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Signal to noise ratio

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Measurement Study of Programmable Network Coding in Cloud-native 5G and Beyond Networks

arXiv

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arXiv 2024年

作者： Lhamo, Osel Doan, Tung V. Tasdemir, Elif Attawna, Mahdi Nguyen, Giang T. Seeling, Patrick Reisslein, Martin Fitzek, Frank H.P. TU Dresden Germany Haptic Communication Systems TU Dresden Germany Germany Department of Computer Science Central Michigan University United States School of Electrical Computer and Energy Engineering Arizona State University United States

Emerging 5G/6G use cases span various industries, necessitating flexible solutions that leverage emerging technologies to meet diverse and stringent application requirements under changing network conditions. The standard 5G RAN solution, retransmission, reduces packet loss but can increase transmission delay in the process. Random Linear Network Coding (RLNC) offers an alternative by proactively sending combinations of original packets, thus reducing both delay and packet loss. Current research often only simulates the integration of RLNC in 5G while we implement and evaluate our approach on real commercially available hardware in a real-world deployment. We introduce Flexible Network Coding (FlexNC), which enables the flexible fusion of several RLNC protocols by incorporating a forwarder with multiple RLNC nodes. Network operators can configure FlexNC based on network conditions and application requirements. To further boost network programmability, our Recoder in the Network (RecNet) leverages intermediate network nodes to join the coding process. Both the proposed algorithms have been implemented on OpenAirInterface and extensively tested with traffic from different applications in a real network. While FlexNC adapts to various application needs of latency and packet loss, RecNet significantly minimizes packet loss for a remote user with minimal increase in delay compared to pure RLNC. © 2024, CC BY.

关键词： Packet loss

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New Combinatorial Insights for Monotone Apportionment

arXiv

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arXiv 2024年

作者： Cembrano, Javier Correa, José Schmidt-Kraepelin, Ulrike Tsigonias-Dimitriadis, Alexandros Verdugo, Victor Department of Algorithms and Complexity Max Planck Institute for Informatics Germany Department of Industrial Engineering Universidad de Chile Chile Department of Mathematics and Computer Science TU Eindhoven Netherlands European Central Bank Germany Institute for Mathematical and Computational Engineering Department of Industrial and Systems Engineering PUC Chile Chile

The apportionment problem constitutes a fundamental problem in democratic societies: How to distribute a fixed number of seats among a set of states in proportion to the states’ populations? This—seemingly simple—task has led to a rich literature and has become well known in the context of the US House of Representatives. In this paper, we connect the design of monotone apportionment methods to classic problems from discrete geometry and combinatorial optimization and explore the extent to which randomization can enhance proportionality. We first focus on the well-studied family of stationary divisor methods, which satisfy the strong population monotonicity property, and show that this family produces only a slightly superlinear number of different outputs as a function of the number of states. While our upper and lower bounds leave a small gap, we show that—surprisingly—closing this gap would solve a long-standing open problem from discrete geometry, known as the complexity of k-levels in line arrangements. The main downside of divisor methods is their violation of the quota axiom, i.e., every state should receive ⌊qi⌋ or ⌈qi⌉ seats, where qi is the proportional share of the state. As we show that randomizing over divisor methods can only partially overcome this issue, we propose a relaxed version of divisor methods in which the total number of seats may slightly deviate from the house size. By randomizing over these methods, we can simultaneously satisfy population monotonicity, quota, and ex-ante proportionality. Finally, we turn our attention to quota-compliant methods that are house-monotone, i.e., no state may lose a seat when the house size is increased. We provide a polyhedral characterization based on network flows, which implies a simple description of all ex-ante proportional randomized methods that are house-monotone and quota-compliant. Copyright © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization

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Effects of Cropping vs Resizing on the Performance of Brain Tumor Segmentation Models

Effects of Cropping vs Resizing on the Performance of Brain ...

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International Conference on computer, Information and Telecommunication systems (CITS)

作者： Ma. Sheila A. Magboo Andrei D. Coronel Department of Physical Sciences and Mathematics College of Arts and Sciences University of the Philippines Manila Padre Faura Ermita Manila Philippines Department of Information Systems and Computer Science School of Science and Engineering Ateneo de Manila University Quezon City Philippines

ISBN: (数字)9798350359091

ISBN: (纸本)9798350359107

This study investigated the effects of image resizing vs cropping on the performance of state-of-the-art models for the brain tumor segmentation task. This is particularly important since many studies simply resize the image without thinking about potential effects of image distortion on the model's segmentation performance. Since the objective of tumor segmentation is to predict the pixels that comprise the actual tumor, image cropping was performed in order to focus more on the brain and the tumor and not on the background. This conjecture was tested using state-of-the-art models namely 2D U-Net, 2D U- Net with VGG19 as backbone, 2D U-Net with InceptionV3 ad backbone, and 2D U-Net with InceptionResNetV2 as backbone. Three different configurations were designed for this purpose. The first configuration used resized images while the second configuration used cropped images. The third configuration used pretrained weights of models of trained on the resized images and then applied them on the cropped images. Overall, the top three models are 2D U-Net with InceptionResNetV2 as backbone trained using the resized images followed by 2D U-Net trained also using the resized images and then finally by 2D U-Net trained using the cropped images. As to why cropping did not perform well in this experiment, several plausible explanations were provided in this study.

关键词： Image segmentation Computational modeling Brain modeling Distortion Telecommunications Task analysis Tumors

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PointGait: Boosting End-to-End 3D Gait Recognition with Point Clouds via Spatiotemporal Modeling

PointGait: Boosting End-to-End 3D Gait Recognition with Poin...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Rui Wang Chuanfu Shen Chao Fan George Q. Huang Shiqi Yu Department of Computer Science and Engineering Southern University of Science and Technology WeBank Institute of Financial Technology Shenzhen Audencia Financial Technology Institute Shenzhen University Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Department of Industrial and Systems Engineering The Hong Kong Polytechnic University

LiDAR is a new type of sensor used for gait recognition. Previous LiDAR-based state-of-the-art methods mostly exploit gait features from the depth maps generated by projecting point clouds in a 3D-to-2D manner, rather than directly using the raw 3D point data. However, these projection-based methods require an additional preprocessing step, which obstructs the universality of the method among different types of LiDARs. On the other hand, while existing point-based methods have achieved promising results in 3D object recognition, they have underperformed in 3D gait recognition, indicating the presence of a domain gap between coarse-grained 3D object classification and fine-grained 3D pedestrians recognition. By analyzing the success achieved by camera-based methods, we perceive that point-based gait recognition fails mainly because of neglecting to capture local representation. To address this issue, we propose an end-to-end 3D gait recognition framework named PointGait, which can directly capture informative gait features from point cloud data. Specifically, PointGait is a multi-stream model consisting of a Global and Local Gait Feature Extractor to extract holistic and fine-grained spatial features. Besides, a Personalized Motion Extractor is introduced to capture inter-frame motion features. Our experimental results on a LiDAR gait dataset, SUSTech1K, outperform all popular point-based methods, demonstrating the effectiveness and potential of our approach. In conclusion, the proposed PointGait promotes the development of point-based gait recognition by highlighting the importance of incorporating fine-grained spatiotemporal information.

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Tilted Least-Squares Parameter Estimation of Linear Regression Models in the Presence of Outliers

Tilted Least-Squares Parameter Estimation of Linear Regressi...

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IEEE Conference on Decision and Control

作者： Biqiang Mu Er-Wei Bai Wei Xing Zheng Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering University of Iowa Iowa City Iowa USA School of Computer Data and Mathematical Sciences Western Sydney University Sydney NSW Australia

The least squares estimator is the most popular identification method. In the absence of prior knowledge on the unknown noise, uniform weights on all samples are often as-sumed. In reality, potentially unknown contamination is always present and the uniform weights are not necessarily the best. Further, explicit information about the nature of contamination is usually absent. To this end, a relaxed-tilted least squares method is proposed here to assign unequal weights so that the effect of undesired noise contamination can be mitigated. The relaxed-tilted least squares method tilts the uniform prior on the samples so as to move the uniform distribution in a direction that enjoys the smallest estimation error in the neighborhood of the uniform distribution. Theoretical results are established including the ability of outlier removal and the guaranteed parameter convergence in the presence of outliers. Numerical algorithms are proposed and simulated, which support the theoretical derivations.

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Automated Colorectal Polyps Detection from Endoscopic Images using MultiResUNet Framework with Attention Guided Segmentation

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Human-Centric Intelligent systems 2024年第2期4卷 299-315页

作者： Ahamed, Md. Faysal Islam, Md. Rabiul Nahiduzzaman, Md. Chowdhury, Muhammad E. H. Alqahtani, Abdulrahman Murugappan, M. Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Department of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Department of Electrical Engineering Qatar University Doha Qatar Department of Biomedical Technology College of Applied Medical Sciences Al-Kharj Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Medical Equipment Technology College of Applied Medical Science Majmaah University Majmaah City Saudi Arabia Intelligent Signal Processing (ISP) Research Lab Department of Electronics and Communication Engineering Kuwait College of Science and Technology Doha Kuwait Department of Electronics and Communication Engineering Vels Institute of Sciences Technology and Advanced Studies Chennai India DCenter for Unmanned Autonomous Systems (CoEUAS) Universiti Malaysia Perlis Perlis Malaysia

The early detection of colorectal polyps is crucial for the reduction of mortality rates. However, manually identifying polyps is time-consuming and expensive, increasing the risk of missing them. Our paper aims to address this issue by presenting an automated segmentation approach for colorectal polyps. This paper proposes a method that combines a skip connection with hybrid attention guidance (AG) using attention guidance (AG) and residual path frameworks to identify salient features. Furthermore, we augment test samples using original, horizontal flip, and vertical flip transformations to enhance model robustness through Test Time Augmentation (TTA). The model was trained with Kvasir-seg samples and evaluated on Kvasir-seg and CVC-ClinicDB datasets to gauge generalizability. A significant accuracy (0.9546), a Dice Similarity Coefficient (DSC) of 0.8557, a Cross-section over Union (IoU) of 0.8824, a Recall (0.8221), a Precision (0.8922), an area under Receiver Operating Characteristics (ROC-AUC) of 0.9454, and an area under Precision-Recall (AUC-PR) of 0.8717 were achieved without TTA. Through TTA integration, accuracy (0.9993), DSC (0.8663), IoU (0.8277), Recall (0.8060), Precision (0.9364), and ROC-AUC (0.9587) have been improved. A comparison of our framework with state-of-the-art models demonstrated its effectiveness and segmentation capabilities. Additionally, the proposed model contains only 0.47 million parameters and a weight size of 6.71 MB, illustrating its potential for clinical diagnostics. A computer-aided diagnosis (CAD) system improves patient outcomes by detecting colorectal polyps early and improving segmentation accuracy.

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Seropill: Novel Minimally Invasive Ingestible Capsule for Serotonin Sensing in the GI Tract 22

Seropill: Novel Minimally Invasive Ingestible Capsule for Se...

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22nd International Conference on Solid-State Sensors, Actuators and Microsystems, Transducers 2023

作者： Straker, Michael A. Levy, Joshua A. Stine, Justin M. Han, Jinjing Beardslee, Luke A. Ghodssi, Reza University Of Maryland Fischell Department Of Bioengineering College ParkMD United States University Of Maryland Institute For Systems Research College ParkMD United States University Of Maryland Fischell Institute For Biomedical Devices College ParkMD United States University Of Maryland Department Of Materials Science And Engineering College ParkMD United States University Of Maryland Department Of Electrical And Computer Engineering College ParkMD United States

ISBN: (纸本)9784886864352

This paper reports the first serotonin (5-HT) sensing ingestible capsule allowing minimally invasive real-time in vivo detection of luminally released serotonin in the gastrointestinal (GI) tract. The system utilizes a surface-mounted sensor with an Au-carbon nanotube (CNT) working electrode, Au counter electrode, and Ag/AgCl reference electrode. The capsule uses previously reported electronics packaged in a 3D-printed ASA shell to perform cyclic voltammetry, allowing the detection and quantification of local serotonin concentration with a dynamic range of 2-10 μM. This device marks a step toward wireless ingestible systems for real-time evaluation of gastrointestinal microbiome metabolites in vivo. © 2023 IEEJ.

关键词： Ingestible Capsule Ingestible Electronics Microbiome Sensing Serotonin Wireless Sensing

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Integrated Preparation of Photonic Spin-Orbit Qudits

Integrated Preparation of Photonic Spin-Orbit Qudits

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Conference on Lasers and Electro-Optics (CLEO)

作者： Haoqi Zhao Yichi Zhang Zihe Gao Jieun Yim Shuang Wu Natalia M. Litchinitser Li Ge Liang Feng Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA USA Department of Materials Science and Engineering University of Pennsylvania Philadelphia PA USA Department of Electrical and Computer Engineering and Department of Physics Duke University Durham NC USA Department of Engineering Science and Physics College of Staten Island CUNY Staten Island NY USA The Graduate Center CUNY New York NY USA

ISBN: (纸本)9798350369311

Preparing and manipulating N-dimensional flying qudits as well as subsequently establishing their entanglement are still challenging tasks. Here, using an integrated approach, we explore the synergy from two degrees of freedom of light, spatial mode and polarization, to generate, encode, and manipulate flying photon qudits in a four-dimensional Hilbert space with high quantum fidelity, intrinsically enabling enhanced noise resilience and higher quantum data rates.

关键词： Integrated optics Polarization Optical polarization Laser noise Laser modes Hilbert space Optical noise Electro-optical waveguides Photonics Resilience

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A Duality-Based Approach to Inverse Kalman Filtering

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IFAC-PapersOnLine 2023年第2期56卷 10258-10263页

作者： Yibei Li Bo Wahlberg Lihua Xie Xiaoming Hu School of Electrical and Electronic Engineering Nanyang Technological University Singapore Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Division of Optimization and Systems Theory Department of Mathematics KTH Royal Institute of Technology Stockholm Sweden

In this paper, the inverse Kalman filtering problem is addressed using a duality-based framework, where certain statistical properties of uncertainties in a dynamical model are recovered from observations of its posterior estimates. The duality relation in inverse filtering and inverse optimal control is established. It is shown that the inverse Kalman filtering problem can be solved using results from a well-posed inverse linear quadratic regulator. Identifiability of the considered inverse filtering model is proved and a unique covariance matrix is recovered by a least squares estimator, which is also shown to be statistically consistent. Effectiveness of the proposed methods is illustrated by numerical simulations.

关键词： Inverse filtering Kalman filters duality covariance estimation identifiability

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