检索结果-内蒙古大学图书馆

21st International System-on-Chip Design Conference, ISOCC 2024

作者： Avitabile, Gianfranco Man, Ka Lok Talarico, Claudio Polytechnic University of Bari Department of Electrical and Information Engineering BA Bari70125 Italy Xi'an Jiaotong-Liverpool University School of Advanced Technology Suzhou215123 China Gonzaga University Department of Electrical and Computer Engineering SpokaneWA99258 United States

ISBN: (纸本)9798350377088

The path loss contribution in a modern telecommunications system is particularly critical due to the higher frequencies employed. However, this can be mitigated by improving the antenna section gain. Adaptive beam steering systems are an enabling technology to seek for this objective, by focusing the antenna pattern towards a given position in the space. In this work, we propose a novel architecture for adaptive beam steering. The phase shift is introduced in the Local Oscillator (LO) path thanks to a modified DDS-PLL structure. The adaptiveness is realized through the Angle-of-Arrival (AoA) estimation of a pilot tone signal. The proposed approach is validated through a simulation campaign involving Hardware-in-the-Loop (HiL) simulations. © 2024 IEEE.

关键词： Hardware-in-the-loop simulation

来源：评论

学校读者我要写书评

暂无评论

Complementary memtransistors for neuromorphic computing: How, what and why

引用

Journal of Semiconductors 2024年第6期45卷 64-80页

作者： Qi Chen Yue Zhou Weiwei Xiong Zirui Chen Yasai Wang Xiangshui Miao Yuhui He School of Integrated Circuits Huazhong University of Science and TechnologyWuhan 430074China Department of Electrical and Computer Engineering University of CaliforniaSan DiegoUSA Dipartimento di Elettronica e Informazione and *** Politecnico di MilanoMilanoItaly Department of Applied Physics Hong Kong Polytechnic UniversityHong KongChina

Memtransistors in which the source-drain channel conductance can be nonvolatilely manipulated through the gate signals have emerged as promising components for implementing neuromorphic *** the other side,it is known that the complementary metal-oxide-semiconductor(CMOS)field effect transistors have played the fundamental role in the modern integrated circuit ***,will complementary memtransistors(CMT)also play such a role in the future neuromorphic circuits and chips?In this review,various types of materials and physical mechanisms for constructing CMT(how)are inspected with their merits and need-to-address challenges *** the unique properties(what)and poten-tial applications of CMT in different learning algorithms/scenarios of spiking neural networks(why)are reviewed,including super-vised rule,reinforcement one,dynamic vision with in-sensor computing,*** exploiting the complementary structure-related novel functions,significant reduction of hardware consuming,enhancement of energy/efficiency ratio and other advan-tages have been gained,illustrating the alluring prospect of design technology co-optimization(DTCO)of CMT towards neuro-morphic computing.

关键词： complementary memtransistor neuromorphic computing reward-modulated spike timing-dependent plasticity remote supervise method in-sensor computing

来源：评论

学校读者我要写书评

暂无评论

A Lightweight Drone Simulator 10

A Lightweight Drone Simulator

引用

10th International Conference on Software Defined Systems, SDS 2023

作者： Blake Putnam, Evan Nihal Senarath, Dylan Ramirez Urquijo, Giselle Tunc, Cihan Bryce, Renee California State Polytechnic University Humboldt Department of Computer Science ArcataCA United States California State Polytechnic University Pomona Department of Electrical and Computer Engineering PomonaCA United States University of North Texas Department of Computer Science and Engineering DentonTX United States

ISBN: (纸本)9798350382235

Drones are valuable assets across industries for a variety of purposes, including but not limited to surveillance, transportation, delivery, smart agriculture, etc. However, drone-related studies may require significant costs to purchase, configure, and deploy to study algorithms and how drones interact with each other. Alternatively, drone simulators can provide a lower-cost option to investigate drones and related algorithms. Nevertheless, drone simulators on the market today tend to be computationally heavy, have a steep learning curve, and may be overwhelming for abstract problem-solving studies. There is a need for an inexpensive and easy testing alternative for researchers and for high-level algorithm development. In this paper, we present a lightweight and easy-to-use drone simulator that allows the definition of multiple drones with their sensors, actuators, speed, and battery limit as well as their communication among themselves. The simulator also allows the definition of the simulation world using small blocks that can be defined using different parameters. We presented two scenarios, such as package delivery and survey area coverage, to show the capabilities of the simulator. The simulator information can be achieved from our group website. © 2023 IEEE.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

Hybrid Simulation of a Wave Energy Converter Power Take Off for Flicker and Power Quality Studies

Hybrid Simulation of a Wave Energy Converter Power Take Off ...

引用

2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Boller, Michael Carapellese, Fabio Brekken, Ted Cotilla-Sanchez, Eduardo Oregon State University School of Electrical Engineering and Computer Science CorvallisOR United States Politecnico di Torino Department of Mechanical and Aerospace Engineering Turin Italy

ISBN: (纸本)9798350316445

This paper presents a hybrid numerical-physical simulation of a point-absorber wave energy converter (WEC) for grid connected power quality studies. The simulated WEC is connected to a laboratory grid through a standard induction generator, with a weak grid simulated by added series impedance between the generator and the laboratory grid connection. Specifically, this paper presents the experimental demonstration of this system to measure power quality metrics including flicker and harmonics and compares the measured results from the hybrid numerical-physical WEC to existing research. © 2023 IEEE.

关键词： Flicker Hybrid-Simulation Power Quality Wave Energy Converter

来源：评论

学校读者我要写书评

暂无评论

Survey on Cryptoprocessors Advances and Technological Trends 3rd

Survey on Cryptoprocessors Advances and Technological Tren...

引用

3rd International Conference on Innovations in Computing Research, ICR 2024

作者： Kakoulli, Elena Zacharioudakis, Eleftherios Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Paphos Cyprus Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine

ISBN: (纸本)9783031655210

Cryptoprocessors play a pivotal role in enhancing the security of modern computing systems by accelerating cryptographic operations and fortifying data protection. This survey delves into the world of cryptoprocessors, encompassing their historical development, architectural features, and various types based on their applications. The paper explores the crucial security considerations surrounding cryptoprocessors, covering both physical and logical aspects, as well as potential threats and countermeasures. Furthermore, the survey navigates through the latest technological trends and advancements in cryptoprocessor design. This includes discussions on post-quantum cryptography, secure enclaves, and mitigating side-channel attacks, all of which are shaping the future of cryptoprocessors. Real-world use cases and applications in domains such as secure communications, the Internet of Things (IoT), and data centers are presented, showcasing the practical significance of cryptoprocessors. However, the paper also highlights the persistent challenges faced by cryptoprocessor designers and provides insights into future research directions aimed at addressing emerging security and performance issues. As the digital landscape continues to evolve, the role of cryptoprocessors becomes increasingly vital in safeguarding sensitive information and fortifying the foundations of cybersecurity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Hybrid Method for Photovoltaic System Forecasting Based on Wavelet Transform and LSTM 2

Hybrid Method for Photovoltaic System Forecasting Based on W...

引用

2nd IEEE International Conference on electrical engineering, computer and Information Technology, ICEECIT 2024

作者： Larasati, Devita Ayu Widjonarko Lin, Tzu-Chiao Chen, Chao-Rong Bachroin, Nabil Prakosa, Setya Widyawan Mujiono Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Electrical Engineering University of Jember Jember Indonesia Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Information Technology State Polytechnic of Jember Jember Indonesia

ISBN: (纸本)9798331504373

Photovoltaic (PV) forecasting is done to help in managing planning and knowing PV energy production. However, PV systems are always associated with fluctuating weather parameters, such as solar irradiance, temperature, wind speed, and humidity. In order, to forecast uncertain data, this object over here study proposed a combination method by using a concept of wavelet transform (WT) and the ability of long-short-term memory (LSTM). The WT is used to decompose the output power with the feature extraction process. Then it is used as input to the LSTM to forecast the futuristic value of output power. The forecasting results are shown in a curve and compared with actual data at several time horizons of 1 day, 15 days, 30 days, and 60 days. In addition, the results are also compared with several other ML methods such as Ridge Regression (RR), Linear Regression (LR), Least Absolute Shrinkage and Selection Operator (LASSO), and Elastic Net (ENET). Each of these treatments shows that the forecasting results using the proposed method are superior. This is also supported by evidence on the percent error values of Root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2). © 2024 IEEE.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

A machine learning-guided design and manufacturing of wearable nanofibrous acoustic energy harvesters

引用

Nano Research 2024年第10期17卷 9181-9192页

作者： Negar Hosseinzadeh Kouchehbaghi Maryam Yousefzadeh Aliakbar Gharehaghaji Safoora Khosravi Danial Khorsandi Reihaneh Haghniaz Ke Cao Mehmet R.Dokmeci Mohammad Rostami Ali Khademhosseini Yangzhi Zhu Terasaki Institute for Biomedical Innovation Los AngelesCA 91367USA Department of Textile Engineering Amirkabir University of Technology(Tehran Polytechnic)Hafez Avenue*** TehranIran Department of Electrical and Computer Engineering University of British ColumbiaVancouverBC V6T1Z4Canada Chemical Sciences Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Computer Science University of Southern CaliforniaLos AngelesCA 90007USA

Nanofibrous acoustic energy harvesters(NAEHs)have emerged as promising wearable platforms for efficient noise-to-electricity conversion in distributed power energy systems and wearable sound amplifiers for assistive listening ***,their reallife efficacy is hampered by low power output,particularly in the low-frequency range(<1 kHz).This study introduces a novel approach to enhance the performance of NAEHs by applying machine learning(ML)techniques to guide the synthesis of electrospun polyvinylidene fluoride(PVDF)/polyurethane(PU)nanofibers,optimizing their application in wearable *** use a feed-forward neural network along with solving an optimization problem to find the optimal input values of the electrospinning(applied voltage,nozzle-collector distance,electrospinning time,and drum rotation speed)to generate maximum output performance(acoustic-to-electricity conversion efficiency).We first prepared a dataset to train the network to predict the output power given the input variables with high *** introducing the neural network,we fix the network and then solve an optimization problem using a genetic algorithm to search for the input values that lead to the maximum energy harvesting *** ML-guided wearable PVDF/PU NAEH platform can deliver a maximal acoustoelectric power density output of 829μW/cm^(3) within the surrounding noise *** addition,our system can function stably in a broad frequency(0.1-2 kHz)with a high energy conversion efficiency of 66%.Sound recognition analysis reveals a robust correlation exceeding 0.85 among lexically akin terms with varying sound intensities,contrasting with a diminished correlation below 0.27 for words with disparate semantic ***,this work provides a previously unexplored route to utilize ML in advancing wearable NAEHs with excellent practicability.

关键词： wearable electronics acoustic energy harvester machine learning piezoelectric nanogenerator electrospun nanofiber

来源：评论

学校读者我要写书评

暂无评论

Board 87: Work in Progress: The 2TO4 Project - Facilitated Transition from 2-Year to 4-Year electrical and computer engineering Studies

Board 87: Work in Progress: The 2TO4 Project - Facilitated T...

引用

2024 ASEE Annual Conference and Exposition

作者： Connor, Kenneth A. Chouikha, Mohamed F. Velez-Reyes, Miguel Sullivan, Barry J. Berhane, Bruk T. Klein, Michelle Gullie, Kathy Ann Muskett, Milford Inclusive Engineering Consortium Rensselaer Polytechnic Institute United States Prairie View A&M University United States University of Texas El Paso United States Electrical & Computer Engineering Department United States Florida International University United States Electrical and Computer Engineering Dept. United States Services LLC United States

The IEC is a nonprofit organization that enables collective efforts through equitable partnerships between its 21 MSI core members, 14 PWI affiliate members and 7 corporate members. The IEC 2TO4 Project builds on its Pathways to Success program to support students who begin their studies at a community college or other 2-year institution by providing financial support (scholarships, internships, academic year stipends), mentoring and other personalized transition support, professional guidance, and community engagement. The 2TO4 network of community colleges (CCs) consists of 20 sub-networks built around the 20 4-year HBCUs, HSIs and TCUs that form most of the core membership of IEC. Generally, a small number (1-3) of CCs located near a core member educate a few students who transfer to the local 4-year MSI ECE program. Some IEC core members have well-developed relationships with their local CCs and see much larger numbers of transfer students. The vision of 2TO4 is to double the total number of students following this pathway to their BS degree in ECE by sharing promising practices and providing robust transition support infrastructure and increased financial support for those CC students who should have MSIs on their radar as they complete their associate degrees. Participating CCs become members of IEC and engage in equitable partnerships with 4-year MSIs and PWIs, industry and DoD labs to implement the various building blocks of 2TO4. During the first year of this multi-year effort, a base version of 2TO4 was created. Program leadership connected with DoDSTEM and the other CC programs it funds, defining the parameters of 2TO4 1.0 (formalizing the relationship between MSI core members and their key local CCs), and began working through institutional challenges with the 60+ program partners. Regular meetings were scheduled, and a general communication infrastructure was rolled out. The first cohort of more than two dozen student participants was selected along wit

关键词： Nonprofit organization

来源：评论

学校读者我要写书评

暂无评论

Personalized Data-Driven State Models of the Circadian Dynamics in a Biometric Signal 46

Personalized Data-Driven State Models of the Circadian Dynam...

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Ike, Chukwuemeka O. Wen, Yunshi Wen, John T. Oishi, Meeko M.K. Brown, Lee K. Julius, A. Agung Rensselaer Polytechnic Institute Department of Electrical Computer and Systems Engineering TroyNY United States Engineering Research Center TroyNY United States University of New Mexico Department of Electrical and Computer Engineering AlbuquerqueNM United States University of New Mexico Department of Internal Medicine AlbuquerqueNM United States

ISBN: (纸本)9798350371499

Circadian rhythms are endogenous 24-hour oscillations that are vital for maintaining our overall well-being. They are driven at a high level by a core circadian clock located in the brain, making their dynamics difficult to track. Various modeling approaches exist to predict the dynamics, but as the models are typically designed on population-level data, their performance is diminished on the individual level. This paper proposes a method for learning personalized latent state models, i.e., dynamical models that explicitly use latent state variables, that relate circadian input(s) to observable biometric signals. Our models combine an autoencoder with a recurrent neural network and use the pair to model the salient dynamics present in the data. We validate our method using experimental data, where the circadian input is light and the biometric data are actigraphy signals. We demonstrate that our method results in models with low-dimensional latent state that can accurately reconstruct and predict the observable biometric signals. Further, we show that the oscillation of the learned latent state agrees with the subjects' circadian clock oscillation as estimated with melatonin *** relevance - This proposes a technique for personal-ized modeling of the circadian system with potential applications in feedback control and individualized circadian studies. © 2024 IEEE.

关键词： Feedback control

来源：评论

学校读者我要写书评

暂无评论

Exploring the Relationship Between Performance and Environmental Sustainability in Measurement Systems: A Preliminary Study 2

Exploring the Relationship Between Performance and Environme...

引用

2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2023

作者： Angrisani, Leopoldo D'Arco, Mauro De Benedetto, Egidio Duraccio, Luigi Esposito, Antonio Imbò, Monica Tedesco, Annarita University of Naples Federico Ii Department of Information Technology and Electrical Engineering Naples Italy Polytechnic University of Turin Department of Electronics and Telecommunications Turin Italy University of Salento Department of Engineering for Innovation Lecce Italy University of Naples Federico Ii Department of Chemistry Naples Italy

ISBN: (纸本)9798350300802

This work focuses on the investigation of the relationship between performance and environmental sustainability in measurement systems. Although it is widely recognized that the use of measurement systems is fundamental in various fields (ranging from healthcare to industry to entertainment), the assessment of the impact of the design and use of a measurement system on the environment is still an open issue. In order to address this aspect, in this work, a dedicated analysis is carried out with a two-fold aim: (i) to offer a methodology for evalu-ating measurement systems considering also their environmental impact, and (ii) to better understand how to continue improving the performance of measurement systems in a non-harmful way for the environment. © 2023 IEEE.

关键词： 4.0 Era Carbon Footprint Climate Change ICT LCA Measurement Monitoring Systems Sustainability Uncertainty

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：