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Deep belief network-hidden Markov model based nonlinear equalizer for VCSEL based optical interconnect

science China(Information sciences) 2020年第6期63卷 155-163页

作者： Fukui TIAN Chuanchuan YANG State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University

The data center has developed rapidly over the past few years, leading to the demand for high speed data transmission. Vertical cavity surface emitting lasers(VCSELs) based optical interconnect is evolving to 100 Gb/s and beyond, which makes nonlinear distortions difficult to be compensated or equalized by conventional equalizers. Moreover, the challenge becomes very complicated for conventional equalizers because of the presence of inter-symbol interference(ISI) together with the nonlinear distortions. So many neural network based DSP algorithms such as artificial neural network(ANN) have been proposed to mitigate the distortions. However, ANN has the limitations that sample's relevant information is not considered,leading to degradation in ANN's performance and high computational complexity. In this paper, in order to maintain an excellent capability of mitigating nonlinear distortions like other neural network based equalizers while considering the sample's relevant information to reduce the computational complexity, we propose a deep belief network-hidden Markov model(DBN-HMM) based nonlinear equalizer which is tested in a PAM-4 modulated VCSEL and multimode fiber(MMF) optical interconnect link experimentally. The BER performance can be greatly improved compared with conventional DSP algorithms. In addition, the computational complexity of DBN-HMM based equalizer can be about 41% lower than that of ANN based method with a similar BER performance.

关键词： short-range optical interconnect vertical cavity surface emitting laser(VCSEL) nonlinear equalizer deep belief network(DBN) hidden Markov model(HMM)

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Employing Feature engineering for River Stage Forecasting to Improve Hybrid Model Performance

Employing Feature Engineering for River Stage Forecasting to...

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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Anna Maria Mihel Špela Pečnik Grega Vrbančič Jonatan Lerga Nino Krvavica Department of Computer Engineering Faculty of Engineering University of Rijeka Rijeka Croatia Intelligent Systems Laboratory Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia Department of Hydrotechnics and Geotechnics Faculty of Civil Engineering University of Rijeka Rijeka Croatia

ISBN: (数字)9789532901382

ISBN: (纸本)9798350354614

Establishing early warning systems and efficient management of water resources in tidal reaches is crucial for achieving adequate flood protection. In tidal reaches, the river stage interacts non-linearly with tides (downstream) and discharge (upstream), making the modeling of river processes extremely complex. Data-driven methods have proven helpful for predicting the river stage parameter. This contrasts with existing numerical approaches, which suffer from several limitations. The aim of this study was to predict the river stage of the upstream part of the tidal river, where the influence of salinity is present during the summer months. Using the CNN-LSTM model trained on historical records of the discharge (station of the variable to be predicted), the river stage of three downstream stations, and the point of interest, the prediction was carried out up to 24 hours in advance. By incorporating feature engineering, we achieved an improvement in model results for the longest horizon, confirmed by standard performance metrics, and considered it efficient and effective for risk mitigation. Introducing feature engineering into the data preprocessing resulted in a predictive performance improvement of 0.98% for the Nash-Sutcliffe Efficiency (NSE) metric, 7.0% for the root mean squared error (RMSE) metric, and 7.25% for the mean absolute error (MAE) metric over the second and third-best scenarios using a spectrogram and time-series data.

关键词： Measurement Weather forecasting Predictive models Wavelet analysis Data models Rivers Numerical models Forecasting Spectrogram Water resources

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A Region Enhanced Discrete Multi-Objective Fireworks Algorithm for Low-Carbon Vehicle Routing Problem

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Complex System Modeling and Simulation 2022年第2期2卷 142-155页

作者： Xiaoning Shen Jiaqi Lu Xuan You Liyan Song Zhongpei Ge Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Institute of Trustworthy Autonomous Systems and also with the Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent ComputationDepartment of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

A constrained multi-objective optimization model for the low-carbon vehicle routing problem(VRP)is established.A carbon emission measurement method considering various practical factors is *** minimizes both the total carbon emissions and the longest time consumed by the sub-tours,subject to the limited number of available *** to the characteristics of the model,a region enhanced discrete multi-objective fireworks algorithm is proposed.A partial mapping explosion operator,a hybrid mutation for adjusting the sub-tours,and an objective-driven extending search are designed,which aim to improve the convergence,diversity,and spread of the non-dominated solutions produced by the algorithm,*** low-carbon VRP instances with different scales are used to verify the effectiveness of the new ***,comparison results with four state-of-the-art algorithms indicate that the proposed algorithm has better performance of convergence and distribution on the low-carbon *** provides a promising scalability to the problem size.

关键词： vehicle routing problem carbon emission multi-objective optimization fireworks algorithm region enhanced

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Pedestrian attribute recognition as label-balanced multi-label learning 24

Pedestrian attribute recognition as label-balanced multi-lab...

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Proceedings of the 41st International Conference on Machine Learning

作者： Yibo Zhou Hai-Miao Hu Yirong Xiang Xiaokang Zhang Haotian Wu Beijing Key Laboratory of Digital Media School of Computer Science and Engineering and State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Beijing Key Laboratory of Digital Media School of Computer Science and Engineering and State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China and Hangzhou Innovation Institute of Beihang University Hangzhou China The University of Manchester UK

Rooting in the scarcity of most attributes, realistic pedestrian attribute datasets exhibit unduly skewed data distribution, from which two types of model failures are delivered: (1) label imbalance: model predictions lean greatly towards the side of majority labels; (2) semantics imbalance: model is easily overfitted on the under-represented attributes due to their insufficient semantic diversity. To render perfect label balancing, we propose a novel framework that successfully decouples label-balanced data re-sampling from the curse of attributes co-occurrence, i.e., we equalize the sampling prior of an attribute while not biasing that of the co-occurred others. To diversify the attributes semantics and mitigate the feature noise, we propose a Bayesian feature augmentation method to introduce true in-distribution novelty. Handling both imbalances jointly, our work achieves best accuracy on various popular benchmarks, and importantly, with minimal computational budget.

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Worst-Case Integrity Attacks and Resilient State Estimation With Partially Secured Measurements

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IEEE Transactions on Control of Network systems 2025年

作者： Zhou, Jing Shang, Jun Chen, Tongwen University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China

This paper examines the problem of optimal deception attacks against state estimation with partially secured measurements, where smart sensors transmit innovation sequences to the remote end for information fusion. Due to resource limitations or defensive countermeasures, the adversary can only modify data packets transmitted through unreliable channels. Meanwhile, the attack should be synthesized with sophistication to deceive an anomaly detector. To investigate the vulnerabilities of such estimation systems without feedback links and enhance security performance, the optimal attack policy is derived by formulating and explicitly solving a convex optimization problem, with the goal of maximizing the sum of estimation errors. Subsequently, a novel attack detection and resilient state estimation algorithm is proposed to ensure an acceptable level of estimation accuracy. The theoretical performance metrics, including false alarm rates for the proposed detector, are provided. Finally, the effectiveness of the results is confirmed through numerical examples. © 2014 IEEE.

关键词： Optimal detection

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Gemini+: Enhancing Real-Time Video Analytics with Dual-Image FPGAs

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年

作者： Wu, Jingrou Hu, Chuang Zhang, Jin Wang, Dan Jiang, Jing University of Technology Sydney Australian Artificial Intelligence Institute SydneyNSW2007 Australia Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems The Department of Computer Science and Engineering Shenzhen518055 China Wuhan University School of Computer Science Wuhan430072 China Southern University of Science and Technology Department of Computer Science and Engineering The Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

Real-time video analytics demand intensive computing resources, often exceeding device capabilities. Heterogeneous computing resources like CPU and GPU, usually work collaboratively to ensure real-time performance. GPU manage data-intensive computing, while CPU handle instruction-intensive tasks. However, analytics accuracy can degrade because of the imbalance between CPU-GPU workloads and resources, and static resources in most systems limit adaptability to dynamic workloads. In addition to CPU-GPU resource control, accuracy is highly dependent on video analytics configuration including resolution, frame rate, and model selection. In this paper, we propose Gemini+, a hardware-accelerated video analytics pipeline empowered by dual-image FPGAs. It enables flexible CPU-GPU resource adaptation by providing near-instantaneous switching between two pre-configured images. We investigate CPU-GPU resource control and video analytics configuration adaptation in a dual-image FPGA-based video analytics pipeline. We study and formulate two problems of practical importance, a single-camera and multi-camera dual computing resource control problem, i.e., SC-DCRC and MC-DCRC. We analyze the problem complexity and develop optimal and sub-optimal algorithms. We evaluate our algorithms through simulation and build a prototype for verification. The results show that Gemini+ can improve analytic accuracy by 25% compared to fixed CPU-GPU resource systems and 88% compared to GPU-dominant systems. © 2025 IEEE.

关键词： Graphics processing unit

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Heuristic-Driven, Type-Specific Embedding in Parallel Spaces for Enhancing Knowledge Graph Reasoning

Heuristic-Driven, Type-Specific Embedding in Parallel Spaces...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yao Liu Yongfei Zhang Xin Wang Shan Yang Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

Knowledge Graph Reasoning aims to derive new insights from existing Knowledge Graphs (KGs) and address any missing or incomplete data. Existing models primarily rely on explicit information while neglecting the implicit constraints imposed by entity types on relations types. For example, when the entity type is "person-person," the relation type should be constrained to interpersonal connections like "co-worker." Based on this perspective, we introduce a priori knowledge-based approach for inferring relations types. This approach utilizes the relations type distribution across different entity types in the dataset to guide the inference process. Additionally, recognizing that mapping all different relations types to a single space can decrease inference accuracy due to the diversity of semantics, we propose a parallel spaces KG embedding model that partitions the entire KG into multiple subspaces. Each subspace is dedicated to learning information associated with a specific relation type. Experimental results on three KG reasoning benchmarks demonstrate that our model outperforms other baselines in accuracy. Importantly, our model shows significant advantages when applied to datasets with a substantial number of relations.

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Analysis of Different Methods for Forecasting Transport Carbon Dioxide Emissions in the Philippines

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Transportation Research Procedia 2025年 82卷 3768-3789页

作者： Aaron Michael Salang Virgilio Ma. Ramos Arse John Salison John Justine Villar Intelligent Transportation Systems Laboratory National Center for Transportation Studies University of the Philippines Diliman Quezon City Philippines Department of Logistics and Information Engineering Tokyo University of Marine Science and Technology Tokyo Japan Scientific Computing Laboratory Department of Computer Science University of the Philippines Diliman Quezon City Philippines

Emission forecasts can be an important way of creating awareness among the public and decision-makers on solving environmental problems. The main goal of this study is to forecast and compare the transport CO2 emissions of the Philippines using four different forecasting models. We use the models of Holt-Winters Exponential Smoothing, Autoregressive Integrated Moving Average (ARIMA), Vector Autoregressive (VAR), and the Artificial Neural Network (ANN). The performance of the different forecasting methods was compared using the coefficient of determination (R2) and the root mean squared error (RMSE) values. Several economic variables from 1990 to 2019 and the transport Carbon Dioxide (CO2) emissions in the Philippines were utilized in this study. The result show that all four methods exhibit goodness of fit and accuracy results according to the statistical measures. In comparison, the multivariate methods (ANN & VAR) performed better than univariate methods (ARIMA & Holt-Winters).

关键词： Transport Emissions Forecasting Time-series modeling ARIMA ANN VAR Holt-Winters

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Partially Constrained GRAND of Linear Block Codes

Partially Constrained GRAND of Linear Block Codes

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International Conference on computers, Communications, and systems (ICCCS)

作者： Yixin Wang Jifan Liang Xiao Ma School of Systems Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

This paper is concerned with a search-number-reduced guessing random additive noise decoding (GRAND) algorithm for linear block codes, called partially constrained GRAND (PC-GRAND). In contrast to the original GRAND, which guesses error patterns without constraints, the PC-GRAND guesses only those error patterns satisfying partial constraints of the codes. In particular, the PC-GRAND takes partial rows of the parity-check matrix as constraints for generating candidate error patterns and the remaining rows as checks for validating the candidates. The number of searches can be reduced when the serial list Viterbi algorithm (SLVA) is implemented for searching over a trellis specified by the partial parity-check matrix. This is confirmed by numerical results. Numerical simulations are also provided for comparison with other decoding algorithms. The parallel implementation of PC-GRAND is also presented in this paper.

关键词： Additive noise Viterbi algorithm Communication systems Numerical simulation Parity check codes Decoding Block codes

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Triple Point Masking

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Liu, Jiaming Kong, Linghe Wu, Yue Gong, Maoguo Li, Hao Miao, Qiguang Ma, Wenping Qin, Can The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Technology Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China The School of Electronic Engineering Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China The College of Mathematics Science Inner Mongolia Normal University Hohhot010022 China The School of Artificial Intelligence Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi’an710071 China The Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

Existing 3D mask learning methods encounter performance bottlenecks under limited data, and our objective is to overcome this limitation. In this paper, we introduce a triple point masking scheme, named TPM, which serves as a scalable plug- and-play framework for MAE pre-training to achieve multi-mask learning for 3D point clouds. Specifically, we augment the baseline methods with two additional mask choices (i.e., medium mask and low mask) as our core insight is that the recovery process of an object can manifest in diverse ways. Previous high-masking schemes focus on capturing the global representation information but lack fine-grained recovery capabilities, so that the generated pre-training weights tend to play a limited role in the fine-tuning process. With the support of the proposed TPM, current methods can exhibit more flexible and accurate completion capabilities, enabling the potential autoencoder in the pre-training stage to consider multiple representations of a single 3D point cloud object. In addition, during the fine-tuning stage, an SVM-guided weight selection module is proposed to fill the encoder parameters for downstream networks with the optimal weight, maximizing linear accuracy and facilitating the acquisition of intricate representations for new objects. Extensive experimental results and theoretical analysis show that five baselines equipped with the proposed TPM achieve comprehensive performance improvements on various downstream tasks. © 2025 IEEE. All rights reserved.

关键词： 3D modeling

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