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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Bolong Liu Hao Zhang Jie Liu Qiang Wang Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Computer Science and Technology Harbin Institute of Technology (ShenZhen) ShenZhen China

Smart agriculture requires an extensive convergence of information technology and agriculture. Attaining intelligence mandates an enormous amount of data to train models. However, it is challenging to acquire a large number of crop image data, limiting the application and growth of computer vision technology in agriculture. To address this problem, we designed a crop image generation system that combines a large language model with visual language multi-modal large models to augment the scale, variety, and resolution of crop image data. First, the system inputs existing real crop images into the visual language multimodal model to extract features and represent crop images in text form. Then, the system passes the crop text representation to the language model for cleaning and processing, which generates prompts to create crop images. The prompts are input into the visual language multi-modal model to generate crop images based on text representation of crops. The resulting crop images undergo image quality evaluation in the visual language multimodal model, and high-quality crop images are saved to the crop image dataset based on the quality evaluation. These steps lead to the formation of the final generated crop image dataset. The experimental results indicate that the crop images generated using the proposed system are similar to but different from the example images. This characteristic enables the expansion of crop data while circumventing redundancy and allowing for resolution control, which is crucial for dense segmentation tasks. Using this method, the existing data can be enlarged up to 7.5 times.

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DPPL Hallway Tracker: Hospital Contact Tracing During the COVID-19 Pandemic 9

DPPL Hallway Tracker: Hospital Contact Tracing During the CO...

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9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Marinoni, Christian Ponzi, Valerio Comminiello, Danilo Dpt. of Information Engineering Electronics and Telecommunications Sapienza University of Rome Via Eudossiana 18 Roma00184 Italy Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

During the COVID-19 pandemic, the use of a people tracking system could have been crucial, particularly in sensitive environments, such as hospitals. DPPL Hallway Tracker is a framework that uses security camera footage to determine which rooms in a corridor a person has entered. It generates a database containing all the people identified and allows quick identification of potential cases of infection based on the time spent in a room and its maximum capacity. DPPL Hallway Tracker is structured in two phases: detection and re-identification. In the first phase, it exploits Mask RCNN to identify people and room doors. In the second one, it uses the deep association metric model from DeepSORT to re-identify a person as he leaves a room. © 2023 Copyright for this paper by its authors.

关键词： COVID-19

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Conformal Prediction for Distribution-free Optimal control of Linear Stochastic Systems

arXiv

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arXiv 2024年

作者： Vlahakis, Eleftherios E. Lindemann, Lars Sopasakis, Pantelis Dimarogonas, Dimos V. The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden The Thomas Lord Department of Computer Science The Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los AngelesCA90089 United States The School of Electronics Electrical Engineering and Computer Science Queen's University Northern Ireland Belfast United Kingdom

We address an optimal control problem for linear stochastic systems with unknown noise distributions and joint chance constraints using conformal prediction. Our approach involves designing a feedback controller to maintain an error system within a prediction region (PR). We define PRs as sublevel sets of a nonconformity score over error trajectories, enabling the handling of joint chance constraints. We propose two methods to design feedback control and PRs: one through direct optimization over error trajectory samples, and the other indirectly using the S-procedure with a disturbance ellipsoid obtained from data. By tightening constraints with PRs, we solve a relaxed problem to synthesize a feedback policy. Our method ensures reliable probabilistic guarantees based on marginal coverage, independent of data size. © 2024, CC BY.

关键词： Optimal control systems

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Traditional Chinese Medicine Health Status Identification with Graph Attention Network 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Fu, Amin Ma, Jishun Wang, Chuansheng Zhou, Changen Li, Zuoyong Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona08034 Spain Research Base of Traditional Chinese Medicine Syndrome Fujian University of Traditional Chinese Medicine Fuzhou350122 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

The Traditional Chinese Medicine Health Status Identification plays an important role in TCM diagnosis and prescription recommendation. In this paper, we propose a method of Status Identification via Graph Attention Network, named SIGAT, which captures the complex medical correlation in the symptom-syndrome graph. More specifically, we construct a symptom-syndrome graph in that symptoms are taken as nodes and the edges are connected by syndromes. And we realize automatic induction of symptom to state element classification by using the attention mechanism and perceptron classifier. Finally, we conduct experiments by using hamming loss, coverage, 0/1 error, ranking loss, average precision, macro-F1 score, and micro-F1 score as evaluation metrics. The results demonstrate that the SIGAT model outperforms comparison algorithms on Traditional Chinese Medicine Prescription Dictionary dataset. The case study results suggest that the proposed method is a valuable way to identify the state element. The application of the graph attention network classification algorithm in TCM health status identification is of high precision and methodological feasibility. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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MINER-RRT*: A Hierarchical and Fast Trajectory Planning Framework in 3D Cluttered Environments

arXiv

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arXiv 2024年

作者： Wang, Pengyu Tang, Jiawei Lin, Hin Wang Zhang, Fan Wang, Chaoqun Wang, Jiankun Shi, Ling Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The Department of Electrical and Computer Engineering The University of Alberta Canada The School of Control Science and Engineering Shandong University Shandong China

—Trajectory planning for quadrotors in cluttered environments has been challenging in recent years. While many trajectory planning frameworks have been successful, there still exists potential for improvements, particularly in enhancing the speed of generating efficient trajectories. In this paper, we present a novel hierarchical trajectory planning framework to reduce computational time and memory usage called MINER-RRT*, which consists of two main components. First, we propose a sampling-based path planning method boosted by neural networks, where the predicted heuristic region accelerates the convergence of rapidly-exploring random trees. Second, we utilize the optimal conditions derived from the quadrotor’s differential flatness properties to construct polynomial trajectories that minimize control effort in multiple stages. Extensive simulation and real-world experimental results demonstrate that, compared to several state-of-the-art (SOTA) approaches, our method can generate high-quality trajectories with better performance in 3D cluttered environments. (Video1) Note to Practitioners—The motivation is the problem of planning trajectories for quadrotor autonomous flight in 3D cluttered and complex scenarios such as wild forest exploration and subterranean environment search-and-rescue. Sampling-based path planning methods are suitable for dealing with the complexity of the physical environment but are not convenient for computing dynamics and their differentials. Optimization-based trajectory generation methods are appropriate for handling various high-order constraints but rely on high-quality initial path solutions. Therefore, this paper combines the advantages of the two methods to propose a novel trajectory planning framework that can generate high-quality trajectories for quadrotors faster than many previous algorithms. We conduct numerous simulations and real-world experiments to verify that our method can be effectively deployed in real scenarios and empo

关键词： Deep neural networks

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Perimeter Defense using a Turret with Finite Range and Service Times

arXiv

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arXiv 2023年

作者： Bajaj, Shivam Bopardikar, Shaunak D. Moll, Alexander Von Torng, Eric Casbeer, David W. Department of Electrical and Computer Engineering Michigan State University United States Department of Computer Science and Engineering Michigan State University United States Control Science Center Air Force Research Laboratory United States

We consider a perimeter defense problem in a planar conical environment comprising a single turret that has a finite range and non-zero service time. The turret seeks to defend a concentric perimeter against N ≥ 2 intruders. Upon release, each intruder moves radially towards the perimeter with a fixed speed. To capture an intruder, the turret’s angle must be aligned with that of the intruder’s angle and must spend a specified service time at that orientation. We address offline and online versions of this optimization problem. Specifically, in the offline version, we establish that in general parameter regimes, this problem is equivalent to solving a Travelling Repairperson Problem with Time Windows (TRP-TW). We then identify specific parameter regimes in which there is a polynomial time algorithm that maximizes the number of intruders captured. In the online version, we present a competitive analysis technique in which we establish a fundamental guarantee on the existence of at best (N − 1)-competitive algorithms. We also design two online algorithms that are provably 1 and 2-competitive in specific parameter regimes. © 2023, CC BY-NC-SA.

关键词： Polynomial approximation

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Automatic Identification of Functionally Untestable Cell-Aware Faults in Microprocessors

Automatic Identification of Functionally Untestable Cell-Awa...

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Asian Test Symposium (ATS)

作者： Nikolaos I. Deligiannis Tobias Faller Iacopo Guglielminetti Riccardo Cantoro Bernd Becker Matteo Sonza Reorda Department of Control and Computer Engineering (DAUIN) Politecnico di Torino Turin Italy Department of Computer Science University of Freiburg Freiburg Germany STMicroelectronics Turin Italy

In-field test of microprocessors is a major topic for the industry, especially in the safety-critical domain, where the respective standards mandate high test coverage thresholds. The dominant fault models used are the transition delay and the stuck-at fault model. However, the adoption of very advanced semiconductor technologies to manufacture devices used in safety-critical applications pushes toward considering new fault models that are better suited to catch subtle and age-related defects. Among the other phenomena, latent cell-internal defects emerged as relevant causes for several failures. Hence, the necessity for the Cell-Aware Test (CAT) was born, and the inclusion of the CAT fault model in the latest safety standards. Although CAT amends the issue of the numerous test escapes, it may suffer as well from the presence of functionally untestable faults that may pollute the overall test efficiency with their presence. In this paper, we propose a solution, based on formal methods, for the automatic identification of functionally untestable faults under the Cell-Aware fault model for the case where the DUT is a fully pipelined processor. As a case study, we used the RISC-V processor RI5CY for which we applied the minimum constraints required to ensure a functional behavior to demonstrate the effectiveness and impact of the approach. With the considered constraints, a significant percentage of functionally untestable faults was located in the several modules within the processor. Furthermore, the method allows to flexibly take into account any constraint stemming from the system configuration and the application. The obtained results have been validated by resorting to commercial EDA tools.

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Performance Creativity Enhancement Method Based on Emotional Analysis and Concentration

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第3期8卷 1019-1048页

作者： Liu, Chang Zhang, Longfei Huang, Tianyu Wu, Yufeng Zhang, Fuquan Lin, Qi Department of Computer Science and Technology Beijing Institute of Technology 100081 China Beijing Key Laboratory of Digital Performance and Simulation Technology 100081 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Informatics King’s College London LondonWC2R 2LS United Kingdom

Intelligent performance creativity is a new research direction of the intersection of technology and art. At present, the cutting-edge technologies such as computer simulation, emotional computing and machine learning have been applied to the evaluation and enhancement of intelligent performance creativity. The application of these technologies has greatly increased the diversity of performance forms and the complexity of performance content. For the traditional stage performance, due to the lack of timely feedback from the audience, it is difficult for the director to enhance the creativity effec-tively. In order to solve this problem, this paper proposes concentration level enhancement method (CLEM). The CLEM extracts and analyzes the physiological and emotional characteristics of the audience by collecting the multimodal physiological signal data of the audience. Through the analysis of the features, the concentration information of the audience is obtained and the concentration level (CL) is defined. Based on the physiological and emotional induction mechanism, a performance creativity enhancement strategy is proposed, which enhances the performance creativity in two aspects: " Visual-based" and " Content-based". Through the concentration level enhancement method, compare the audience’s performance attention changes before and after the performance creativity enhancement, select the effective strategies to enhance the performance creativity, and provide the director with quantifiable performance creativity enhancement feedback results. The experimental results show that the CLEM proposed in this paper can effectively induce significant changes in the audience’s emotion, and the selected creative enhancement strategy can significantly improve the audience’s concentration and achieve the purpose of enhancing the creativity of the performance. © 2023, Journal of Network Intelligence.

关键词： Physiology

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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Power Allocation Strategy for Secret Key Generation Method in Wireless Communications

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computers, Materials & Continua 2021年第8期68卷 2179-2188页

作者： Bin Zhang Muhammad Waqas Shanshan Tu Syed Mudassir Hussain Sadaqat Ur Rehman School of Electronics and Information Engineering Hunan University of Science and EngineeringYongzhou425199China Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering Ghulam Ishaq Khan(GIK)Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Electronics Engineering FICTBalochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan Department of Computer Science Namal InstituteMianwali42200Pakistan

Secret key generation(SKG)is an emerging technology to secure wireless communication from ***,the SKG at the physical layer is an alternate solution over traditional cryptographic methods due to wireless channels’***,the physical layer secret key generation(PHY-SKG)depends on two fundamental parameters,i.e.,coherence time and power *** coherence time for PHY-SKG is not applicable to secure wireless *** is because coherence time is for a certain period of ***,legitimate users generate the secret keys(SKs)with a shorter key length in ***,an attacker can quickly get information about the ***,the attacker can easily get valuable information from authentic ***,we considered the scheme of power allocation to enhance the secret key generation rate(SKGR)between legitimate ***,we propose an alternative method,i.e.,a power allocation,to improve the *** results show 72%higher SKGR in bits/sec by increasing power *** addition,the power transmission is based on two important parameters,i.e.,epsilon and power loss factor,as given in power transmission *** found out that a higher value of epsilon impacts power transmission and subsequently impacts the *** SKGR is approximately 40.7%greater at 250 from 50 mW at epsilon=*** value of SKGR is reduced to 18.5%at 250 mW when epsilonis ***,the transmission power is also measured against the different power loss factor values,i.e.,3.5,3,and 2.5,respectively,at epsilon=***,it is concluded that the value of epsilon and power loss factor impacts power transmission and,consequently,impacts the SKGR.

关键词： Secret key generation rate power allocation physical layer wireless communication

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