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arXiv 2021年

作者： Li, Longyuan Zhang, Jihai Yan, Junchi Jin, Yaohui Zhang, Yunhao Duan, Yanjie Tian, Guangjian MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China State Key Lab of Advanced Optical Communication System and Network Shanghai Jiao Tong University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Huawei Noah’s Ark Lab China

Time-series is ubiquitous across applications, such as transportation, finance and healthcare. Time-series is often influenced by external factors, especially in the form of asynchronous events, making forecasting difficult. However, existing models are mainly designated for either synchronous time-series or asynchronous event sequence, and can hardly provide a synthetic way to capture the relation between them. We propose Variational Synergetic Multi-Horizon Network (VSMHN), a novel deep conditional generative model. To learn complex correlations across heterogeneous sequences, a tailored encoder is devised to combine the advances in deep point processes models and variational recurrent neural networks. In addition, an aligned time coding and an auxiliary transition scheme are carefully devised for batched training on unaligned sequences. Our model can be trained effectively using stochastic variational inference and generates probabilistic predictions with Monte-Carlo simulation. Furthermore, our model produces accurate, sharp and more realistic probabilistic forecasts. We also show that modeling asynchronous event sequences is crucial for multi-horizon time-series forecasting. © 2021, CC BY.

关键词： Time series

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Comprehensive Analysis of Secure Data Aggregation Scheme for Industrial Wireless Sensor Network

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computers, Materials & Continua 2019年第8期61卷 583-599页

作者： Weidong Fang Wuxiong Zhang QianQian Zhao Xiaohong Ji Wei Chen Biruk Assefa Key Laboratory of Wireless Sensor Network&Communication Shanghai Institute of Micro-system and Information TechnologyChinese Academy of SciencesShanghai201899China Shanghai Research Center for Wireless Communication Shanghai201210China College of Physics and Electronic Information Engineering Qinghai University for NationalitiesXining810007China School of Computer Science and Technology China University of Mining and TechnologyXuzhou221116China Infromation Communication Technology Department Wollo UniversityDessie EthiopiaEthiopia

As an Industrial Wireless Sensor Network(IWSN)is usually deployed in a harsh or unattended environment,the privacy security of data aggregation is facing more and more ***,the data aggregation protocols mainly focus on improving the efficiency of data transmitting and aggregating,alternately,the aim at enhancing the security of *** performances of the secure data aggregation protocols are the trade-off of several metrics,which involves the transmission/fusion,the energy efficiency and the security in Wireless Sensor Network(WSN).Unfortunately,there is no paper in systematic analysis about the performance of the secure data aggregation protocols whether in IWSN or in *** consideration of IWSN,we firstly review the security requirements and techniques in WSN data aggregation in this ***,we give a holistic overview of the classical secure data aggregation protocols,which are divided into three categories:hop-by-hop encrypted data aggregation,end-to-end encrypted data aggregation and unencrypted secure data *** this way,combining with the characteristics of industrial applications,we analyze the pros and cons of the existing security schemes in each category qualitatively,and realize that the security and the energy efficiency are suitable for ***,we make the conclusion about the techniques and approach in these categories,and highlight the future research directions of privacy preserving data aggregation in IWSN.

关键词： Industrial wireless sensor network wireless sensor network cyber security secure data aggregation protocol

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A wideband wearable antenna using DGS method with bending and wet performance analysis at sub-6 GHz 5G band

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AIP Conference Proceedings 2024年第1期3161卷

作者： S. K. Noor R. Nurulazlina C. H. C. Alexander T. Islam Advanced Communication Engineering (ACE) Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Malaysia Centre for Advanced Electrical & Electronic System (CAEES) Faculty of Engineering Built Environment and Information Technology SEGi University 47610 Petaling Jaya Selangor Malaysia Asia Pacific University of Technology and Innovation School of Engineering Jalan Teknologi 5 Taman Teknologi Malaysia 57000 Kuala Lumpur Malaysia Department of Electrical and Computer Engineering University of Houston Texas USA

In this paper, it introduces a compact (44mm x 50mm) wearable antenna made of fabric that operates within the sub-6 GHz range for 5G New Radio technology. The concept of Defected Ground Structure (DGS) was implemented to achieve wider bandwidth. The antenna relies on a felt textile base with a relative permittivity of 1.45 and a thickness of 3 millimeters. This base is then sandwiched between two layers of a special electrically conductive fabric called “Super shield it” for the ground and radiating planes. This new antenna design offers several advantages over similar wearable antennas for sub-6 GHz 5G. Unlike previous designs, this antenna maintains high performance even when bent or wet, making it more durable for real-world use. Moreover, the simulated bandwidth obtained was 140 MHz (3.42 to 3.56 GHz) at frees space flat condition and measured bandwidth was 1700 MHz which covers n77 and n78 band of the sub-6 GHz. The proposed antenna obtained an overall simulated gain of 2.67 dBi and directivity of 7.15 dBi when placed on the four-layer human phantom. The proposed wearable antenna covered the desired frequency range when subjected to bending, partially wet and fully wet. Lastly, the obtained Specific Absorption Rate (SAR) is 0.106 W/Kg which makes the proposed antenna safe to be used for body- centric communication.

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Angle estimation for terahertz ultra-massive MIMO-based space-to-air communications

arXiv

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arXiv 2021年

作者： Liao, Anwen Gao, Zhen Yang, Yang Nguyen, Ha H. Wang, Hua Yin, Hao School of Information and Electronics Beijing Institute of Technology Beijing100081 China Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing100081 China School of Information and Communication Engineering BUPT Beijing100876 China Department of Electrical and Computer Engineering University of Saskatchewan SaskatoonSKS7N 5A9 Canada Institute of China Electronic System Engineering Corporation Beijing100141 China

This paper investigates terahertz ultra-massive (UM)-MIMO-based angle estimation for space-to-air communications, which can solve the performance degradation problem caused by the dual delay-beam squint effects of terahertz UM-MIMO channels. Specifically, we first design a grouping true-time delay unit module that can significantly mitigate the impact of delay-beam squint effects to establish the space-to-air THz link. Based on the subarray selection scheme, the UM hybrid array can be equivalently considered as a low-dimensional fully-digital array, and then the fine estimates of azimuth/elevation angles at both UAVs and satellite can be separately acquired using the proposed prior-aided iterative angle estimation algorithm. The simulation results that close to Cramér-Rao lower bounds verify the effectiveness of our solution. Copyright © 2021, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network

arXiv

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arXiv 2022年

作者： Wang, Muhan Zhang, Zhe Wang, Yue Gao, Silin Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Electrical and Computer Engineering Department George Mason University FairfaxVA22030 United States

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving three-dimensional reconstruction along the elevation direction from multiple observations. In recent years, compressed sensing (CS) technique has been introduced into TomoSAR considering for its super-resolution ability with limited samples. Whereas, the CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity and complex parameter fine-tuning. Among the different CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a robust reconstruction approach, however, the parameters in the ISTA algorithm are manually chosen, which usually requires a time-consuming fine-tuning process to achieve the best performance. Aiming at efficient TomoSAR imaging, a novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed towards the TomoSAR imaging problem in this paper, and the key parameters of ISTA are learned from training data via deep learning to avoid complex parameter fine-tuning and significantly relieves the training burden. In addition, experiments verify that it is feasible to use traditional CS algorithms as training labels, which provides a tangible supervised training method to achieve better 3D reconstruction performance even in the absence of labeled data in real applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Compressed sensing

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An Efficient Evolutionary Algorithm for High-Speed Train Rescheduling Under a Partial Station Blockage

SSRN

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SSRN 2023年

作者： Wang, Rongsheng Zhang, Qi Dai, Xuewu Yuan, Zhiming Zhang, Tao Ding, Shuxin Jin, Yaochu China Academy of Railway Sciences Corporation Limited Beijing100081 China Signal and Communication Research Institute China Academy of Railway Sciences Corporation Limited Beijing100081 China Train Operation Control Laboratory for High-Speed Railway National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail Transit Beijing100081 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Faculty of Technology Bielefeld University BielefeldD-33615 Germany Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

This paper investigates the high-speed train rescheduling (HSTR) problem under a partial station blockage and proposes an efficient problem-specific strengthen elitist genetic algorithm (PS-SEGA) for HSTR. An HSTR model that is subject to train operation constraints is established to minimize the total train delay. The PS-SEGA framework consists of the following main steps. Firstly, a permutation-based encoding method is developed to define an efficient search space based on a train departure sequence. A decoding method is employed to output the rescheduled timetable and objective value based on the departure sequence. Moreover, a hybrid initialization method involving an efficient heuristic strategy (EHS) is put forward to accelerate the convergence speed of Evolutionary algorithms (EAs). Using problem-specific knowledge, EHS generates an efficient and feasible solution for the initial population. Finally, a restart strategy is presented to maintain genetic diversity. Compared with other advanced EAs and their improved variants also using the PS-SEGA framework, experimental results demonstrate the effectiveness of the proposed PS-SEGA for addressing HSTR scenarios under the partial station blockage. As for the scenarios that CPLEX can not obtain optimal solutions within 10 minutes, PS-SEGA can all provide quasi-optimal solutions in real-time. Furthermore, compared with the other two strategies (i.e., First-Scheduled-First-Served and EHS), PS-SEGA can find the best departure sequence that can output the timetable with a quasi-optimal solution. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

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ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年第4期10卷 425-436页

作者： Ganesan, Saampras Barrios, Fernando A. Batta, Ishaan Bauer, Clemens C.C. Braver, Todd S. Brewer, Judson A. Brown, Kirk Warren Cahn, Rael Cain, Joshua A. Calhoun, Vince D. Cao, Lei Chetelat, Gaël Ching, Christopher R.K. Creswell, J. David Dagnino, Paulina Clara Davanger, Svend Davidson, Richard J. Deco, Gustavo Dutcher, Janine M. Escrichs, Anira Eyler, Lisa T. Fani, Negar Farb, Norman A.S. Fialoke, Suruchi Fresco, David M. Garg, Rahul Garland, Eric L. Goldin, Philippe Hafeman, Danella M. Jahanshad, Neda Kang, Yoona Khalsa, Sahib S. Kirlic, Namik Lazar, Sara W. Lutz, Antoine McDermott, Timothy J. Pagnoni, Giuseppe Piguet, Camille Prakash, Ruchika S. Rahrig, Hadley Reggente, Nicco Saccaro, Luigi F. Sacchet, Matthew D. Siegle, Greg J. Tang, Yi-Yuan Thomopoulos, Sophia I. Thompson, Paul M. Torske, Alyssa Treves, Isaac N. Tripathi, Vaibhav Tsuchiyagaito, Aki Turner, Matthew D. Vago, David R. Valk, Sofie Zeidan, Fadel Zalesky, Andrew Turner, Jessica A. King, Anthony P. Department of Biomedical Engineering The University of Melbourne Carlton VIC Australia Contemplative Studies Centre Melbourne School of Psychological Sciences The University of Melbourne Melbourne VIC Australia Systems Lab of Neuroscience Neuropsychiatry and Neuroengineering The University of Melbourne Parkville VIC Australia Universidad Nacional Autónoma de México Instituto de Neurobiolgía México Querétaro Mexico Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology and Emory University Atlanta GA United States Department of Psychology Northeastern University Boston Massachusetts Brain and Cognitive Science McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge Massachusetts Department of Psychological and Brain Sciences Washington University St. Louis Missouri Department of Behavioral and Social Sciences Brown University School of Public Health Providence Rhode Island Department of Psychology Carnegie Mellon University Pittsburgh PA United States University of Southern California Department of Psychiatry & Behavioral Sciences Los Angeles California University of Southern California Center for Mindfulness Science Los Angeles California Institute for Advanced Consciousness Studies Santa Monica California Department of Psychiatry and Behavioral Health The Ohio State University College of Medicine Columbus OH United States Normandie University Université de Caen Normandie INSERM U1237 Neuropresage Team Cyceron Caen France Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of the University of Southern California Los Angeles California Desert-Pacific Mental Illness Research Education and Clinical Center VA San Diego Healthcare System San Diego California Computational Neuroscience Group Center for Brain and Cognition Department of Information and Communication Technologies Universitat Pompeu Fa

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)–Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the f

关键词： Consortium ENIGMA Magnetic resonance imaging (MRI) Meditation Mindfulness Neuroimaging

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A preliminary usability evaluation for online annotation and student clustering system 27

A preliminary usability evaluation for online annotation and...

引用

27th International Conference on computers in Education, ICCE 2019

作者： Chang, Miao-Han Kuo, Rita Essalmi, Fathi Chang, Maiga Kumar, Vive Kung, Hsu-Yang School of Computing and Information System Athabasca University Canada Department of Computer Science and Engineering New Mexico Institute of Mining and Technology United States Research Unit of Technologies of Information and Communication University of Kairouan Tunisia Department of Management Information System National Pingtung University of Science and Technology Taiwan

ISBN: (纸本)9789869721448

This research evaluated an annotation recommendation system – GRACE (General Rapid Annotation Clustering Enhancement) – which gives suggestions of what annotations might be missing when students were reading the teaching materials. A pilot is done in two computer science classes in a college in Tunisia. The results show that system’s usability is acceptable for students doing online reading activities on GRACE. © ICCE 2019 - 27th International Conference on computers in Education, Proceedings. All rights reserved.

关键词： Students

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Design of a Cryptographically Secure Pseudo Random Number Generator with Grammatical Evolution

Research Square

引用

Research Square 2021年

作者： Ryan, Conor Kshirsagar, Meghana Vaidya, Gauri Cunningham, Andrew Sivaraman, R. Biocomputing and Developmental Systems Group Lero The Science Foundation Ireland Research Centre for Software Computer Science and Information System Department University of Limerick LimerickV94 T9PX Ireland Intel Research and Development Ireland Limited LeixlipW23 CX68 Ireland Department of Electronics Communication Engineering School of Electrical and Electronics Engineering Shanmugha Arts Science Technology and Research Academy Deemed to be University Thanjavur613401 India

This work investigates the potential of evolving an initial seed with Grammatical Evolution (GE), for the construction of cryptographically secure (CS) pseudo-random number generator (PRNG). We harness the flexibility of GE as an entropy source for returning initial seeds. The initial seeds returned by GE demonstrate an average entropy value of 7.920261600000001 which is extremely close to the ideal value of 8. The initial seed combined with our proposed approach, control_flow_incrementor, is used to construct both, GE-PRNG and GE-CSPRNG. The random numbers generated with CSPRNG meet the prescribed National Institute of Standards and Technology (NIST) SP800-22 requirements. Monte Carlo simulations established the efficacy of the PRNG. The experimental setup was designed to estimate the value for pi, in which 100,000,000 random numbers were generated by our system and which resulted in returning the value of pi to 3.146564000, with a precision up to six decimal digits. The random numbers by GE-PRNG were compared against those generated by Python’s rand() function for sampling. The sampling results, when measured for accuracy against twenty-nine real world regression datasets, showed that GE-PRNG had less error when compared to Python’s rand() against the ground truths in seventeen of those, while there was no discernible difference in the remaining twelve. © 2021, CC BY.

关键词： Monte Carlo methods

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BPS2025 - Rapid and accurate transepithelial resistance measurement of bronchiolar epithelium using impedance spectroscopy and RCRC model

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Biophysical Journal 2025年第3期124卷 296a-297a页

作者： Athena Chien Colby Lewallen Guiying Cui Analia Vazquez Cegla Erica Lull Hanna Khor Nael A. McCarty Craig Forest Walter H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University Atlanta GA USA National Eye Institute National Institutes of Health Bethesda MD USA Department of Pediatrics Emory University Atlanta GA USA Department of Pharmacology Emory University Atlanta GA USA School of Mechanical Engineering Georgia Institute of Technology Atlanta GA USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Picture Archives Communication System Emory University School of Medicine Atlanta GA USA

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