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2022 Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2022

作者： Stepchenkov, Yury A. Khilko, Dmitry Shikunov, Yury Orlov, Georgy Federal Research Center Computer Science and Control Russian Academy of Sciences Institute of Informatics Problems Moscow Russia

ISBN: (纸本)9781665409933

The FPGA prototype of the Hybrid Architecture of Recurrent Signal Processor has shown promising results on the isolated word recognition (IWR) problem. This task is a subset of DSP algorithms. In expanding trial on a wide range of DSP algorithms, work has begun to increase the versatility of architectural solutions. The proposed solutions aim to reduce memory costs for storing tagged input data and optimize a number of computational pipeline modules. The article covers the implementation results of the FFT and a series of FIR filter algorithms. Some redundant, highly specialized for the IWR tasks blocks were excluded in the course of this work. The proposed architectural solutions increase the scalability of the architecture and reuse the introduced blocks to optimize the solution of other DSP tasks. © 2022 IEEE.

关键词： Fast Fourier transforms

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Parameterization of Nonlinear System Matrices for Implementation of State-Dependent Coefficient Control Methods

Parameterization of Nonlinear System Matrices for Implementa...

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2023 International Russian Automation Conference, RusAutoCon 2023

作者： Kabanov, Aleksey A. Sevastopol State University Department of Informatics and Control in Technical Systems Sevastopol Russia Federal Research Center "Computer Science and Control"of Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9798350345551

The research is devoted to the methods of state-dependent coefficient (SDC) for solving nonlinear optimal control problems. An important stage in the application of SDС methods is the parameterization (or even say factorization) of the original nonlinear system to the SDC form. In past studies, it has been shown that for multidimensional systems, the number of such parameterizations is infinite. Choosing the right parameterization can provide better convergence and quality parameters of the corresponding SDC method. This paper presents a number of criteria that can be used for parameterization. By the example of the problem of modeling large-scale monsoon intra-seasonal fluctuations based on the solution of the optimal control problem in a finite time interval, the analysis of the influence of the choice of the appropriate parameterization criterion on the solution of the problem is carried out. © 2023 IEEE.

关键词： Factorization

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IoT system for AQI monitoring and control

IoT system for AQI monitoring and control

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2024 International Conference Automatics and informatics, ICAI 2024

作者： Ganchev, Ivan Ji, Zhanlin University of Plovdiv "Paisii Hilendarski" Plovdiv Bulgaria Bulgarian Academy of Sciences Institute of Mathematics and Informatics Sofia Bulgaria Limerick Ireland Zhejiang A&f University College of Mathematics and Computer Science Hangzhou China

ISBN: (纸本)9798350353907

A horizontal-type IoT platform, named EMULSION, is presented*, which was developed for fast IoT system prototyping and speedy roll-out of respective IoT services. The successful use of the platform as a basis of various pilot systems, operating in different IoT areas, is show-cased on the example of 'smart environment' monitoring and control. © 2024 IEEE.

关键词： Emulsification

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Using Self-Supervised Dual Constraint Contrastive Learning for Cross-Modal Retrieval 26

Using Self-Supervised Dual Constraint Contrastive Learning f...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Wang, Xintong Li, Xiaoyu Ding, Liang Zhao, Sanyuan Biemann, Chris Language Technology Group Department of Informatics University of Hamburg Germany School of Computer Sciences and Technology Beijing Institute of Technology China Department of Computer Science Technical University Berlin Germany Jd Explore Academy China

ISBN: (纸本)9781643684369

In this work, we present a self-supervised dual constraint contrastive method for efficiently fine-tuning the vision-language pre-trained (VLP) models that have achieved great success on various cross-modal tasks, since full fine-tune these pre-trained models is computationally expensive and tend to result in catastrophic forgetting restricted by the size and quality of labeled datasets. Our approach freezes the pre-trained VLP models as the fundamental, generalized, and transferable multimodal representation and incorporates lightweight parameters to learn domain and task-specific features without labeled data. We demonstrated that our self-supervised dual contrastive model performs better than previous fine-tuning methods on MS COCO and Flickr 30K datasets on the cross-modal retrieval task, with an even more pronounced improvement in zero-shot performance. Furthermore, experiments on the MOTIF dataset prove that our self-supervised approach remains effective when trained on a small, out-of-domain dataset without overfitting. As a plug-and-play method, our proposed method is agnostic to the underlying models and can be easily integrated with different VLP models, allowing for the potential incorporation of future advancements in VLP models. © 2023 The Authors.

关键词： Zero-shot learning

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Hand Gesture Recognition Using a Multi-modal Deep Neural Network 13th

Hand Gesture Recognition Using a Multi-modal Deep Neural Net...

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13th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2024

作者： Fulsunder, Saneet Umar, Saidu Taherkhani, Aboozar Liu, Chang Yang, Shengxiang School of Computer Science and Informatics De Montfort University Leicester United Kingdom Key Laboratory of Networked Control Systems and Digital Factory Department Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9783031579189

As devices around us get more intelligent, new ways of interacting with them are sought to improve user convenience and comfort. While gesture-controlled systems have existed for some time, they either use additional specialized imaging equipment, require unreasonable computing resources, or are simply not accurate enough to be a viable alternative. In this work, a reliable method of recognizing gestures is proposed. The built model correctly classifies hand gestures for keyboard typing based on the activity captured by an ordinary camera. Two models are initially developed for classifying video data and classifying time-series sequences of the skeleton data extracted from a video. The models use different strategies of classification and are built using lightweight architectures. The two models are the baseline models which are integrated to form a single multi-modal model with multiple inputs, i.e., video and time-series inputs, to improve accuracy. The performances of the baseline models are then compared to the multimodal classifier. Since the multimodal classifier is based on the initial models, it naturally inherits the benefits of both baseline architectures and provides a higher testing accuracy of 100% compared to the accuracy of 85% and 75% for the baseline models respectively. © IFIP International Federation for Information Processing 2024.

关键词： Deep neural networks

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A Robust Preprocessing Method for Measuring Image Visual Quality Using Log-Polar FFT Features 8th

A Robust Preprocessing Method for Measuring Image Visual Qua...

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8th International Congress on Artificial Intelligence and Pattern Recognition, IWAIPR 2023

作者： Chen, Guang Yi Krzyzak, Adam Valev, Ventzeslav Department of Computer Science and Software Engineering Concordia University MontrealQCH3G 1M8 Canada Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia1113 Bulgaria

ISBN: (纸本)9783031495519

To register two images, existing methods normally estimate the parameters of an affine transform, and then perform an inverse affine transform to the test image by using the estimated parameters. Any metric can then be applied to the reference image and the normalized image. In this paper we propose a new method for measuring visual image quality which circumvents the need to estimate the parameters of the affine transform. Instead, we use the log-polar transform and the fast Fourier transform (FFT) to extract features that are invariant to translation, rotation, and scaling. We apply the existing structural similarity index measure (SSIM) to the two invariant feature images, where no inverse transform is needed. Experimental results show that our proposed method outperforms the standard metric, the mean SSIM (MSSIM), significantly in terms of visual quality scores. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image visual quality quality metrics rotation invariance scale invariance translation invariance

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Retain and Adapt: Online Sequential EEG Classification With Subject Shift

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4479-4492页

作者： Duan, Tiehang Wang, Zhenyi Shen, Li Doretto, Gianfranco Adjeroh, Donald A. Li, Fang Tao, Cui Mayo Clinic Department of Artificial Intelligence and Informatics JacksonvilleFL32216 United States University of Maryland College ParkMD20740 United States Jd Explore Academy Beijing100101 China West Virginia University Lane Department of Computer Science and Electrical Engineering MorgantownWV26506 United States

Large variance exists in Electroencephalogram (EEG) signals with its pattern differing significantly across subjects. It is a challenging problem to perform online sequential decoding of EEG signals across different subjects, where a sequence of subjects arrive in temporal order and no signal data is jointly available beforehand. The challenges include the following two aspects: 1) the knowledge learned from previous subjects does not readily fit to future subjects, and fast adaptation is needed in the process;and 2) the EEG classifier could drastically erase information of learnt subjects as learning progresses, namely catastrophic forgetting. Most existing EEG decoding explorations use sizable data for pretraining purposes, and to the best of our knowledge we are the first to tackle this challenging online sequential decoding setting. In this work, we propose a unified bi-level meta-learning framework that enables the EEG decoder to simultaneously perform fast adaptation on future subjects and retain knowledge of previous subjects. In addition, we extend to the more general subject-agnostic scenario and propose a subject shift detection algorithm for situations that subject identity and the occurrence of subject shifts are unknown. We conducted experiments on three public EEG datasets for both subject-aware and subject-agnostic scenarios. The proposed method demonstrates its effectiveness in most of the ablation settings, e.g. an improvement of 5.73% for forgetting mitigation and 3.50% for forward adaptation on SEED dataset for subject agnostic scenarios. © 2020 IEEE.

关键词： Brain computer interface

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RF-PSSM:A Combination of Rotation Forest Algorithm and Position-Specific Scoring Matrix for Improved Prediction of Protein-Protein Interactions Between Hepatitis C Virus and Human

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Big Data Mining and Analytics 2023年第1期6卷 21-31页

作者： Xin Liu Yaping Lu Liang Wang Wei Geng Xinyi Shi Xiao Zhang School of Medical Informatics and Engineering Xuzhou Medical UniversityXuzhou 221000China College of Computer Science and Technology China University of Mining and TechnologyXuzhou 221116China Laboratory Medicine Guangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesGuangzhou 510080China Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhou 310005China

The identification of hepatitis C virus(HCV)virus-human protein interactions will not only help us understand the molecular mechanisms of related diseases but also be conductive to discovering new drug *** increasing number of clinically and experimentally validated interactions between HCV and human proteins have been documented in public databases,facilitating studies based on computational *** this study,we proposed a new computational approach,rotation forest position-specific scoring matrix(RF-PSSM),to predict the interactions among HCV and human *** particular,PSSM was used to characterize each protein,two-dimensional principal component analysis(2DPCA)was then adopted for feature extraction of ***,rotation forest(RF)was used to implement *** results of various ablation experiments show that on independent datasets,the accuracy and area under curve(AUC)value of RF-PSSM can reach 93.74% and 94.29%,respectively,outperforming almost all cutting-edge *** addition,we used RF-PSSM to predict 9 human proteins that may interact with HCV protein E1,which can provide theoretical guidance for future experimental studies.

关键词： protein-protein interactions hepatitis C virus position specific scoring matrix two-dimensional principal component analysis rotation forest

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Self-Timed Counter Implementation Basis

Self-Timed Counter Implementation Basis

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Igor Sokolov Yuri Stepchenkov Yuri Diachenko Institute of Informatics Problems of FRC “Computer Science and Control” of Russian Academy of Sciences Moscow Russia

The article researches the problem of self-timed (ST) binary counter implementation. ST circuits demonstrate correct operation over a much wider range of supply voltage and ambient temperature, in contrast to synchronous counterparts. The reason for this is hardware redundancy, two-phase operating discipline and mandatory acknowledging of the switch completion of all circuit cells in each phase of circuit operation. As a result, ST circuits operate stably no matter the cell delays. Due to their simpler indication, serial ST counters have less hardware redundancy than combinational ST circuits. However, they require the specific procedure organization to implement ST preset. The article examines the circuitry basis for the ST counter implementation and proposes a schematic preset realization ensuring its self-timing and optimal hardware complexity.

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Multi-threshold Hysteresis-Based Congestion Control for UAV-Based Detection Sensor Network 22nd

Multi-threshold Hysteresis-Based Congestion Control for UAV...

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22nd International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2022

作者： Aliyu, Bashir Mokrov, Evgeny Samouylov, Konstantin 6 Miklukho-Maklaya st. Moscow117198 Russia Institute of Informatics Problems Federal Research Center "Computer Science and Control" of Russian Academy of Sciences Moscow119333 Russia

ISBN: (纸本)9783031302572

Nowadays, unmanned aerial vehicles (UAV) are considered for a variety of different applications. One of the important ones is automatic collection of sensory data. In this work, we study the use of UAV-based for secure remote monitoring network enabled via LoRaWAN lower-power wide area (LPWA) network. In the considered use-case the UAVs carry sensors collecting data and LoRa modules to transmit collected data to the stationary gateway. The work focuses on congestion control on the gateway. Specifically, we propose a multi-threshold hysteresis-based control mechanism to alleviate system overload by dropping some of the data send by the sensors. The considered system is modeled using the queuing system with multiple thresholds. The work studies time-dependent characteristics of the system: average time the system spends in overload and reduced load states. Our results demonstrate that using hysteresis control allows for the system to accept priority data traffic even under relatively high system load. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Wide area networks

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