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arXiv 2023年

作者： Cereda, Elia Crupi, Luca Risso, Matteo Burrello, Alessio Benini, Luca Giusti, Alessandro Pagliari, Daniele Jahier Palossi, Daniele The Dalle Molle Institute for Artificial Intelligence USI SUPSI Lugano6962 Switzerland The Department of Control and Computer Engineering Politecnico di Torino Turin10129 Italy The Department of Electrical Electronic and Information Engineering University of Bologna Bologna40136 Italy The Integrated Systems Laboratory ETH Zürich Zürich8092 Switzerland

Miniaturized autonomous unmanned aerial vehicles (UAVs) are an emerging and trending topic. With their form factor as big as the palm of one hand, they can reach spots otherwise inaccessible to bigger robots and safely operate in human surroundings. The simple electronics aboard such robots (sub-100 mW) make them particularly cheap and attractive but pose significant challenges in enabling onboard sophisticated intelligence. In this work, we leverage a novel neural architecture search (NAS) technique to automatically identify several Pareto-optimal convolutional neural networks (CNNs) for a visual pose estimation task. Our work demonstrates how real-life and field-tested robotics applications can concretely leverage NAS technologies to automatically and efficiently optimize CNNs for the specific hardware constraints of small UAVs. We deploy several NAS-optimized CNNs and run them in closed-loop aboard a 27-g Crazyflie nano-UAV equipped with a parallel ultra-low power System-on-Chip. Our results improve the State-of-the-Art by reducing the in-field control error of 32% while achieving a real-time onboard inference-rate of ∼10 Hz@10 mW and ∼50 Hz@90 mW. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Graph-based Simultaneous Localization and Bias Tracking

arXiv

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arXiv 2023年

作者： Venus, Alexander Leitinger, Erik Tertinek, Stefan Meyer, Florian Witrisal, Klaus The Signal Processing and Speech Communication Laboratory Graz University of Technology Graz Austria The Christian Doppler Laboratory for Location-aware Electronic Systems Austria NXP Semiconductors Gratkorn Austria The Department of Electrical and Computer Engineering and Scripps Institution of Oceanography University of California San Diego San DiegoCA United States

We present a factor graph formulation and particle-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The proposed sequential algorithm jointly estimates the mobile agent’s position together with a time-varying number of multipath components (MPCs). The MPCs are represented by "delay biases" corresponding to the offset between line-of-sight (LOS) component delay and the respective delays of all detectable MPCs. The delay biases of the MPCs capture the geometric features of the propagation environment with respect to the mobile agent. Therefore, they can provide position-related information contained in the MPCs without explicitly building a map of the environment. We demonstrate that the position-related information enables the algorithm to provide high-accuracy position estimates even in fully obstructed line-of-sight (OLOS) situations. Using simulated and real measurements in different scenarios we demonstrate that the proposed algorithm significantly outperforms state-of-the-art multipath-aided tracking algorithms and show that the performance of our algorithm constantly attains the posterior Cramér-Rao lower bound (P-CRLB). Furthermore, we demonstrate the implicit capability of the proposed method to identify unreliable measurements and, thus, to mitigate lost tracks. © 2023, CC BY.

关键词： Mobile agents

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Advances of wearable silicone rubber-based triboelectric nanogenerators: from manufacturing to application

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International Journal of Extreme Manufacturing 2025年第03期 131-164页

作者： Dianlong Shen Taili Du Fangyang Dong Hu Cai Aziz Noor Xiannan Du Yongjiu Zou Chengkuo Lee Minyi Xu Dalian Key Laboratory of Marine Micro/Nano Energy and Self-Powered Systems Marine Engineering CollegeDalian Maritime University State Key Laboratory of Maritime Technology and Safety Collaborative Innovation Research Institute of Autonomous Ship Dalian Maritime University Department of Electrical and Computer Engineering National University of Singapore

Wearable bioelectronic devices are rapidly evolving towards miniaturization and multifunctionality, with remarkable features such as flexibility and comfort. However, achieving a sustainable power supply for wearable bioelectronic devices is still a great *** nanogenerators(TENGs) provide an efficient solution by converting irregular,low-frequency bioenergy from the human body into electrical energy. Beyond sustainably powering wearable bioelectronics, the harvested electrical energy also carries rich information for human body sensing. In this conversion process, the choice of material plays a crucial role in affecting the output performance of the TENGs. Among various materials, silicone rubber(SR)stands out due to its exceptional plasticity, flexibility, comfortability and other favorable properties. Moreover, with appropriate treatment, SR can achieve extreme functionalities such as high robustness, good stability, self-healing capabilities, rapid response, and more. In this review, recent advances in wearable SR-based TENGs(SR-TENGs) are systematically reviewed with a focus on their application in different parts of the human body. Given that the manufacturing method of SR-TENGs largely determines its output performance and sensitivity,this paper introduces the design of SR-TENGs, including material selection, process modulation, and structure optimization. Additionally, this article discusses the current challenges in the SR-TENG fabrication technology and potential future directions,aiming to promote the effective development of SR-TENGs in biomechanical energy harvesting and self-powered sensing applications.

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On the Generalization of Machine-Learning-aided QoT Estimation in Optical Networks

On the Generalization of Machine-Learning-aided QoT Estimati...

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International Conference on Wireless and Optical Communications ( WOCC)

作者： Hanyu Gao Liang Zhang Bin Zhang Xiaoliang Chen Zhaohui Li Guangdong Provincial Key Labratory of Optoelectronic Information Processing Chips and Systems School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Southern Marine Science and Engineering Guangdong Laboratory Zhuhai China Department of Electrical and Computer Engineering George Mason University Fairfax VA USA

This paper presents a composable machine learning method for generalizing the quality-of-transmission (QoT) metric estimation in optical networks. The composable machine learning approach characterizes this metric for lightpaths of arbitrary lengths by compositions of launch, propagation and readout modules. Results verify the feasibility of the design and show its successful application in facilitating autonomous lightpath provisioning.

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Inverse-designed multi-wavelength, multi-mode optical interconnects using soliton microcombs

Inverse-designed multi-wavelength, multi-mode optical interc...

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CLEO: Science and Innovations, S and I 2022

作者： Yang, K.Y. White, A.D. Ashtiani, F. Shirpurkar, C. Pericherla, S.V. Chang, L. Song, H. Zou, K. Zhou, H. Pang, K. Yang, J. Guidry, M.A. Lukin, D.M. Hao, H. Trask, L. Ahn, G.H. Netherton, A. Briles, T.C. Stone, J.R. Rechtman, L. Stone, J.S. Van Gasse, K. Skarda, J.L. Su, L. Vercruysse, D. MacLean, J.P.W. Aghaeimeibodi, S. Li, M.-J. Miller, D.A.B. Marom, D.M. Papp, S.B. Willner, A.E. Bowers, J.E. Delfyett, P.J. Aflatouni, F. Vučković, J. E.L.Ginzton Laboratory Stanford University StanfordCA United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States The College of Optics and Photonics University of Central Florida OrlandoFL32816 United States Department of Electrical and Computer Engineering University of California Santa BarbaraCA United States Department of Electrical Engineering University of Southern California Los AngelesCA United States Time and Frequency Division National Institute of Standards and Technology BoulderCO United States Applied Physics Department The Hebrew University of Jerusalem Jerusalem Israel Corning Incorporated CorningNY United States Department of Physics University of Colorado BoulderCO United States

We demonstrate 130 Gbps transmission in each of 4 spatial modes using Si3N4 soliton microcombs and inverse-designed silicon mode multiplexers. Out of 52 carriers, 42 data channels show natively error-free data transmi... 详细信息

ISBN: (纸本)9781557528209

关键词： Solitons

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Active Simultaneously Transmitting and Reflecting Surface Assisted NOMA Networks

arXiv

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arXiv 2024年

作者： Yue, Xinwei Xie, Jin Ouyang, Chongjun Liu, Yuanwei Shen, Xia Ding, Zhiguo The Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100101 China The Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China The School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Beijing100191 China The Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Department of Electrical Engineering Princeton University Princeton United States

The novel active simultaneously transmitting and reflecting surface (ASTARS) has recently received a lot of attention due to its capability to conquer the multiplicative fading loss and achieve full-space smart radio environments. This paper introduces the ASTARS to assist non-orthogonal multiple access (NOMA) communications, where the stochastic geometry theory is used to model the spatial positions of pairing users. We design the independent reflection/transmission phase-shift controllers of ASTARS to align the phases of cascaded channels at pairing users. We derive new closed-form and asymptotic expressions of the outage probability and ergodic data rate for ASTARS-NOMA networks in the presence of perfect/imperfect successive interference cancellation (pSIC). The diversity orders and multiplexing gains for ASTARS-NOMA are derived to provide more insights. Furthermore, the system throughputs of ASTARS-NOMA are investigated in both delay-tolerant and delay-limited transmission modes. The numerical results are presented and show that: 1) ASTARS-NOMA with pSIC outperforms ASTARS assisted-orthogonal multiple access (ASTARS-OMA) in terms of outage probability and ergodic data rate;2) The outage probability of ASTARS-NOMA can be further reduced within a certain range by increasing the power amplification factors;3) The system throughputs of ASTARS-NOMA are superior to that of ASTARS-OMA in both delay-limited and delay-tolerant transmission modes. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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STAR-RIS Assisted Covert Wireless Communications with Randomly Distributed Blockages

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4690-4705页

作者： Li, Xingwang Zhao, Jingwei Chen, Gaojie Hao, Wanming Da Costa, Daniel Benevides Nallanathan, Arumugam Shin, Hyundong Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454099 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Shenzhen518107 China 5GIC 6GIC University of Surrey United Kingdom Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom Kyung Hee University Department of Electronic Engineering Yongin-si17104 Korea Republic of Kyung Hee University Department of Electronics and Information Convergence Engineering Korea Republic of Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore

As one of the promising technologies, reconfigurable intelligent surface (RIS) and simultaneous transmitting and reflecting RIS (STAR-RIS) have attracted great interest. However, the existing RISs offer broadband tuning capability without filtering function due to the absence of radio frequency (RF) units, which easily leads to the unexpected tuning of the RIS undesired signals, especially in large-scale deployments. For the target network, it is difficult to obtain the parameter settings of RISs to serve other networks, which causes the unpredictability of the wireless environment. In this paper, we consider the covert communication in a STAR-RIS assisted random wireless network with randomly distributed blockages. We investigate the impact of STAR-RIS large-scale deployment on covert communication and leverage its inherent unpredictability for improving the covertness. We derive the average detection error probability for warden within the random wireless networks. Furthermore, we optimize the passive beamforming of STAR-RIS to maximize the covert communication rate, considering both direct and indirect line-of-sight (LoS) links. To address this, we employ an alternating optimization (AO) algorithm based on the semi-definite programming (SDP) method. Finally, numerical results demonstrate significant enhancements and increase covert capability achieved through the large-scale deployment of STAR-RIS. 2002-2012 IEEE.

关键词： Numerical methods

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Merging synthetic and real embryo data for advanced AI predictions

arXiv

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arXiv 2024年

作者： Presacan, Oriana Dorobanţiu, Alexandru Thambawita, Vajira Riegler, Michael A. Stensen, Mette H. Iliceto, Mario Aldea, Alexandru C. Sharma, Akriti Faculty of Electronics Telecommunications and Information Technology National University of Science and Technology Politehnica Bucharest Bucharest061071 Romania Department of Computer Science and Electrical Engineering Lucian Blaga University of Sibiu Sibiu550024 Romania Department of Holistic Systems SimulaMet Stensberggata 27 Oslo0170 Norway Department of Computer Science Oslo Metropolitan University Oslo0167 Norway Volvat Spiren Oslo0176 Norway Faculty of Biotechnologies University of Agronomic Sciences and Veterinary Medicine Bucharest011464 Romania Department of Validation Intelligence for Autonomous Software Systems Simula Research Laboratory Oslo0164 Norway Cyber Security Simula Research Laboratory Oslo0164 Norway

Accurate embryo morphology assessment is essential in assisted reproductive technology for selecting the most viable embryo. Artificial intelligence has the potential to enhance this process. However, the limited availability of embryo data presents challenges for training deep learning models. To address this, we trained two generative models using two datasets—one we created and made publicly available, and one existing public dataset—to generate synthetic embryo images at various cell stages, including 2-cell, 4-cell, 8-cell, morula, and blastocyst. These were combined with real images to train classification models for embryo cell stage prediction. Our results demonstrate that incorporating synthetic images alongside real data improved classification performance, with the model achieving 97% accuracy compared to 94.5% when trained solely on real data. This trend remained consistent when tested on an external Blastocyst dataset from a different clinic. Notably, even when trained exclusively on synthetic data and tested on real data, the model achieved a high accuracy of 92%. Furthermore, combining synthetic data from both generative models yielded better classification results than using data from a single generative model. Four embryologists evaluated the fidelity of the synthetic images through a Turing test, during which they annotated inaccuracies and offered feedback. The analysis showed the diffusion model outperformed the generative adversarial network, deceiving embryologists 66.6% versus 25.3% and achieving lower Fréchet inception distance scores. © 2024, CC BY-SA.

关键词： Generative adversarial networks

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An Improved Machine Learning Technique with Effective Heart Disease Prediction System

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computers, Materials & Continua 2021年第12期69卷 4169-4181页

作者： Mohammad Tabrez Quasim Saad Alhuwaimel Asadullah Shaikh Yousef Asiri Khairan Rajab Rihem Farkh Khaled Al Jaloud College of Computing and Information Technology University of BishaBisha67714Saudi Arabia King Abdulaziz City for Science and Technology P.O.Box 6086Riyadh11442Saudi Arabia College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Department of Electrical Engineering Laboratory for AnalysisConception and Control of SystemsLR-11-ES20National Engineering School of TunisTunis El Manar University1002Tunisia

Heart disease is the leading cause of death *** heart disease is challenging because it requires substantial experience and *** research studies have found that the diagnostic accuracy of heart disease is *** coronary heart disorder determines the state that influences the heart valves,causing heart *** indications of coronary heart disorder are strep throat with a red persistent skin rash,and a sore throat covered by tonsils or strep *** work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial *** first,we achieved the component perception measured by using a hybrid cuckoo search particle swarm optimization(CSPSO)*** this perception measure,characterization and accuracy were improved,while the execution time of the proposed model was *** CSPSO-deep recurrent neural network algorithm resolved issues that state-of-the-art methods *** proposed method offers an illustrative framework that helps predict heart attacks with high *** proposed technique demonstrates the model accuracy,which reached 0.97 with the applied dataset.

关键词： Machine learning deep recurrent neural network effective heart disease prediction framework

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Computing Safest $st$-Paths in Backbone Networks: Efficiently Solvable Cases and Fast Heuristics

Computing Safest $st$-Paths in Backbone Networks: Efficientl...

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International Workshop on Reliable Networks Design and Modeling (RNDM)

作者： Balázs Vass Péter Revisnyei Alija Pašíć Department of Telecommunications and Artificial Intelligence (TMIT) Faculty of Electrical Engineering and Informatics (VIK) High Speed Networks Laboratory (HSNLab) Budapest University of Technology and Economics (BME) Budapest Hungary Faculty of Mathematics and Computer Science Babeș-Bolyai University Cluj Napoca Romania HUN-REN-BME Information Systems Research Group Budapest Hungary

ISBN: (数字)9798331518189

ISBN: (纸本)9798331518196

For proper evaluation and optimization of the expected availability of a backbone network service, two related very fundamental modeling and algorithmic questions are the following. 1) Realistically estimate the availability of a given $st$ -path for some source and target pair of communicating nodes $s$ and $t$ , and 2) Find a safest $st$ -path. For these, traditional approaches suppose network element failures are independent. In this paper, we show that, by not considering joint failure probabilities, the traditional approaches may misguess the $st$ -path availabilities and, consequently, the total connection availability, which can lead to more frequent Service Level Agreement (SLA) violations and a financial burden on the Communication Service Provider (CSP). Due to the inconsistent estimations, the supposedly safest paths yielded by these approaches may turn out to be suboptimal. On the positive side, we propose a fast algorithmic approach, that, under some assumptions, returns with a (truly) safest $st$ -path accompanied by its exact expected availability. When the aforementioned assumptions do not hold, in our simulations, the proposed algorithmic scheme proved to be a good heuristic, performing at least as well as the traditional method.

关键词： Computational modeling Heuristic algorithms Conferences Estimation Hazards Computational efficiency Optimization Service level agreements

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