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Machine learning (ML) 2

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Automatic modulation classification 1

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Frequency of arrival (FOA) 1

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Monte Carlo importance sampling (MCIS) 1

Multiple-input multiple-output 1

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Passive source localization 1

SW management 1

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Space-time block code 1

Time of arrival (TOA) 1

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State-of-the-art applications of machine learning in the life cycle of solid waste management

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 4, doi: 10.1007/s11783-023-1644-x

Abstract:

● State-of-the-art applications of machine learning (ML) in solid waste

Keywords： Machine learning (ML) Solid waste (SW) Bibliometrics SW management Energy utilization Life cycle

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Aneffective approach for low-complexity maximumlikelihood based automatic modulation classification of Article

Maqsood H. SHAH, Xiao-yu DANG

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 3, Pages 465-475 doi: 10.1631/FITEE.1800306

Abstract: A low-complexity likelihood methodology is proposed for automatic modulation classification of orthogonal space-time block code (STBC) based multiple-input multiple-output (MIMO) systems. We exploit the zero-forcing equalization technique to modify the typical average likelihood ratio test (ALRT) function. The proposed ALRT function has a low computational complexity compared to existing ALRT functions for MIMO systems classification. The proposed approach is analyzed for blind channel scenarios when the receiver has imperfect channel state information (CSI). Performance analysis is carried out for scenarios with different numbers of antennas. Alamouti-STBC systems with 2 ×2 and 2 ×1 and space-time transmit diversity with a 4 ×4 transmit and receive antenna configuration are considered to verify the proposed approach. Some popular modulation schemes are used as the modulation test pool. Monte-Carlo simulations are performed to evaluate the proposed methodology, using the probability of correct classification as the criterion. Simulation results show that the proposed approach has high classification accuracy at low signal-to-noise ratios and exhibits robust behavior against high CSI estimation error variance.

Keywords： Multiple-input multiple-output Space-time block code Maximum likelihood Automatic modulation classification Zero-forcing

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Artificial intelligence algorithms for cyberspace security applications: a technological and status review Review

Jie CHEN, Dandan WU, Ruiyun XIE,chenjie1900@mail.nwpu.edu.cn,wudd@cetcsc.com

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 8, Pages 1117-1142 doi: 10.1631/FITEE.2200314

Abstract: Three technical problems should be solved urgently in : the timeliness and accuracy of network attack detection, the credibility assessment and prediction of the security situation, and the effectiveness of security defense strategy optimization. algorithms have become the core means to increase the chance of security and improve the network attack and defense ability in the application of . Recently, the breakthrough and application of AI technology have provided a series of advanced approaches for further enhancing network defense ability. This work presents a comprehensive review of AI technology articles for applications, mainly from 2017 to 2022. The papers are selected from a variety of journals and conferences: 52.68% are from Elsevier, Springer, and IEEE journals and 25% are from international conferences. With a specific focus on the latest approaches in , , and some popular s, the characteristics of the algorithmic models, performance results, datasets, potential benefits, and limitations are analyzed, and some of the existing challenges are highlighted. This work is intended to provide technical guidance for researchers who would like to obtain the potential of AI technical methods for and to provide tips for the later resolution of specific issues, and a mastery of the current development trends of technology and application and hot issues in the field of network security. It also indicates certain existing challenges and gives directions for addressing them effectively.

Keywords： Artificial intelligence (AI) Machine learning (ML) Deep learning (DL) Optimization algorithm Hybrid

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Passive source localization using importance sampling based on TOA and FOA measurements Article

Rui-rui LIU, Yun-long WANG, Jie-xin YIN, Ding WANG, Ying WU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 8, Pages 1167-1179 doi: 10.1631/FITEE.1601657

Abstract: Passive source localization via a maximum likelihood (ML) estimator can achieve a high accuracy but involvestheorem and Monte Carlo importance sampling (MCIS) to achieve an approximate global solution to the MLGaussian distribution, which is called an important function for efficient sampling, to approximate the ML

Keywords： arrival (TOA) Frequency of arrival (FOA) Monte Carlo importance sampling (MCIS) Maximum likelihood (ML