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A deep learning-based model for automated STN localization using local field potentials in Parkinson’s disease

Soft Computing 2025年第4期29卷 2343-2362页

作者： Hosny, Mohamed Naeem, Mohamed A. Zhu, Minwei Gao, Wenpeng Elshenhab, Ahmed M. Fu, Yili Department of Electrical Engineering Benha Faculty of Engineering Benha University Benha Egypt State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150080 China Department of Mechatronics and Robotics Engineering Faculty of Engineering South Valley University Qena Egypt First Affiliated Hospital Harbin Medical University Harbin150001 China School of Life Science and Technology Harbin Institute of Technology Harbin150080 China Department of Mathematics Faculty of Science Mansoura University Mansoura35516 Egypt

Accurate identification of the subthalamic nucleus (STN) borders is time-consuming, relying heavily on the neurosurgeon expertise in manually interpreting the electrophysiological signals. Local field potentials (LFPs) have garnered imperative attention due to their strong correlation with the STN. However, existing detection models often face challenges with high computational complexity, hyperparameter optimization and lack of explainability, making them unreliable for clinicians. Therefore, this study introduces an explanatory framework using convolutional neural networks (CNN) for detecting the STN region from LFPs. Continuous wavelet transform is employed to convert LFPs signals into scalogram images, which are then processed by sixteen CNN models. We evaluated our framework by examining the impact of various limiting factors on the classification performance, including model size, learning rate (LR), optimizers and data scaling. Deep features are extracted from the top-performing CNN architectures to capture rich representations of the scalograms. These features are then fused and classified using k-nearest neighbour algorithm. Gradient-weighted class activation mapping is used to explain the decisions made by the proposed model. Our approach achieved an accuracy of 99.61%, outperforming individual CNN models for STN localization. The experimental results revealed that CNN models, embedded with additional hyperparameters and layers, generally outperformed smaller models. Besides, low LR significantly enhanced the performance compared to high LR. Moreover, features extracted from untuned networks produced lower performance than tuned networks. The proposed system could revolutionize deep brain stimulation surgery by increasing efficiency and reducing reliance on clinician expertise for STN detection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Wavelet transforms

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MDRN: Multi-distillation residual network for efficient MR image super-resolution

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Mathematical Biosciences and Engineering 2024年第10期21卷 7421-7434页

作者： Deng, Liwei Chen, Jingyi Yang, Xin Huang, Sijuan Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration School of Automation Harbin University of Science and Technology Heilongjiang Harbin150080 China State Key Laboratory of Oncology in South China Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Guangdong Provincial Clinical Research Center for Cancer Sun Yat-sen University Cancer Center Guangdong Guangzhou510060 China Guangdong Esophageal Cancer Institute Guangdong Guangzhou510060 China

Super-resolution (SR) of magnetic resonance imaging (MRI) is gaining increasing attention for being able to provide detailed anatomical information. However, current SR methods often use the complex convolutional network for feature extraction, which is difficult to train and not suitable for limited computation resources in the medical scenario. To tackle these bottlenecks, we propose a multidistillation residual network (MDRN) for more differential feature refinement, which has a superior trade-off between reconstruction accuracy and computation cost. Specifically, a novel feature multidistillation residual block with a contrast-aware channel attention module was designed to make the residual features more focused on low-vision information, which maximizes the power of MDRN. Comprehensive experiments demonstrate the superiority of our MDRN over state-of-the-art methods in reconstruction quality and efficiency. Our method outperforms other existing methods in peak signal-noise ratio by up to 0.44-1.82 dB in 4× scale when GPU memory and runtime are lower than in other SR methods. The source code will be available at https://***/Jennieyy/MDRN. © 2024 the Author(s).

关键词： Magnetic resonance imaging

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Programmatic Synthesis Method for Extracted Pole Filters 15

Programmatic Synthesis Method for Extracted Pole Filters

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15th International Conference on Microwave and Millimeter Wave technology, ICMMT 2023

作者： Wu, Qing-Qiang Wu, Di-Si Long, Qiao Gong, Hong-Wei Nan, Shen Shen, Tian State Key Laboratory of Mobile Network and Mobile Multimedia Technology Department of Rhp System Zte Corporation Shen Zhen518055 China

ISBN: (纸本)9798350338874

In this paper, a method of converting in-line filters with frequency-variable couplings into extracted pole filters is studied. The concrete steps and theory are given, and the programmatic synthesis method for the coupling matrix of extracted pole filters is introduced. The extracted pole filter is favored by its flexible structure layout. However, the traditional synthesis of a extracted pole filter requires a large quantity of calculation and extraction steps, and it is very difficult to form a programmatic synthesis process. With the development of frequency-variable coupling technology, the programmatic synthesis of filters with frequency-variable couplings has matured based on matrix rotation and scaling. Based on these principles, this paper further studies how to extend the programmatic synthesis to the extracted pole structures, so as to simplify the design of the extracted pole filters. © 2023 IEEE.

关键词： Flexible structures

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A novel freemium code SAND(v1.0)for generation of randomly packed beds

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Particuology 2024年第12期95卷 198-211页

作者： Nikita Shadymov Viacheslav Papkov Dmitry Pashchenko Samara State Technical University Molodogvardeiskaya 244 StSamara443100Russia Department of Mechanical Engineering(Robotics) Guangdong Technion-Israel Institute of Technology241 Daxue RoadShantouGuangdong515063China Faculty of Mechanical Engineering Technion-Israel Institute of TechnologyTechnion CityHaifa3200003Israel Guangdong Provincial Key Laboratory of Materials and Technologies for Energy Conversion Guangdong Technion—Israel Institute of TechnologyGuangdong515063China

The main current approaches for generation of the packed bed models are based on rigid body dynamics(RBD)and Newton's laws(discrete element methods-DEM).This paper deals with the development and analysis of a novel code based on analytical geometry approach for the packed bed *** architecture and main algorithms of the novel code are described and *** parameters of the packed bed generated via the novel code are compared with experimental data and packed beds generated via Blender(RBD),Yade(DEM).The novel code demonstrates many advantages,such as good correlation with experimental data,no overlaps between pellets in the packed bed,and a low computational time for packed bed *** packed bed model can be directly exported *** *** advantages are also demonstrated and *** novel code is attached to this paper and can be freely used by engineers and scientists.

关键词： Packed bed Discrete element method Python-based code CAD software

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Research on Startup and Emergency Braking Strategy of Doubly-Fed Induction-Machine-Based Flywheel Energy Storage system 26

Research on Startup and Emergency Braking Strategy of Doubly...

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26th International Conference on Electrical Machines and systems, ICEMS 2023

作者： Zhao, Jinglin Li, Hui Xiang, Xuewei Li, Wendong Chongqing University State Key Laboratory of Power Transmission Equipment & System Security and New Technology School of Electrical Engineering Chongqing China

ISBN: (纸本)9798350317589

This paper proposes control strategies for startup and emergency braking procedures of Doubly-Fed InductionMachine-Based Flywheel Energy Storage system (DFIM-FESS). For startup procedure, short-circuit the stator and simplify the stator Flux Oriented Control (FOC) by ignoring the transient part of the voltage equation based on flywheel characteristics, then perform the rotor currents control can output large electromagnetic torque and increasing speed stably. For emergency braking procedure, the rotor is two-phase short-circuited connect to DC source, the stator is connected to the braking resistance, then analyze the constraint conditions of the system, and propose the method to get the suitable parameters of the brake resistance and the DC excitation resource. The simulation results verify the effectiveness of the control strategies and configuration method of equipment parameters. © 2023 IEEE.

关键词： Flywheels

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Photonics-assisted intensity-modulation and direct-detection MIMO millimeter-wave system in the D-band

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Chinese Optics Letters 2025年第1期23卷 7-12页

作者： Jingwen Tan Peng Tian Jianjun Yu Xianming Zhao Xinglin Dou Long Zhang Xiongwei Yang YangHan Yi Wei Bing Zhang Qinyi Zhang Luhan Jiang Weiping Li Rahim Uddin Kaihui Wang Wen Zhou State Key Laboratory of ASIC and System Key Laboratory for Information Science of Electromagnetic Waves(MoE)School of Information Science and TechnologyFudan UniversityShanghai 200433China Beijing Hongshan Information Technology Research Institute Co. Ltd.Beijing 100176China

We experimentally demonstrate the transmission of discrete multi-tone(DMT)millimeter-wave(mm-wave)signals over a 1.2-km distance at the D-band(110–170 GHz)in a cost-effective intensity-modulation and direct-detection(IM/DD)communication *** the experiment,we successfully achieve the transmission of DMT-QPSK and DMT-16QAM mm-wave signals over multiple-input multiple-output(MIMO)*** the 1.2-km free-space transmission,the bit error rate(BER)of the DMT-16QAM is below the 25%soft decision forward error correction(25%SD-FEC)threshold of 4×10-2,with a maximum net bit rate of 24.62 Gbit/s achieved in this system.

关键词： D-band discrete multi-tone intensity modulation and direct detection long-distance wireless transmission photonics-assisted technique

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Estimation and Prediction of CoM With Terrain Feature Embedding During Walking

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 16108-16120页

作者： Haolan Xian Jingfeng Xiong Yuanwen Zhang Xinxing Chen Chenglong Fu Yuquan Leng Key Laboratory of Biomimetic Robotics and Intelligent Systems Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China School of Biomedical Engineering and the State Key Laboratory of Robotics and Systems Harbin Institute of Technology (Shenzhen) Shenzhen China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China

Wearable devices are currently being used to reduce metabolism and assist people with disabilities for daily walking. For the elderly and disabled people, improving the walking stability of wearable devices is a crucial and unsolved research. In particular, the center of mass (CoM) trajectory can reflect the walking state as well as the stability of a person. For this reason, realizing the prediction of CoM trajectory under daily walking is promising to improve the wearable devices. In this article, a method for estimation and prediction of CoM during daily walking was proposed. A visual-inertial-odometry algorithm was used to obtain history CoM trajectories during walking, and the depth information from camera data was extracted by sequential distance embedding method and encode the information into terrain vectors. The trajectory vectors were patched with the corresponding terrain vectors and then realized the fusion of multi-modal data, which were then fed into a well-trained temporal convolution network to output the prediction results. With data from two outdoor datasets, the method in this paper was verified to be able to be used for CoM trajectory prediction for a variety of walking tasks and consume low computational cost. This method has the potential to be extensible in improving the stability of wearable devices, such as exoskeleton, powered prosthetic, and so on. Note to Practitioners—It is necessary to obtain the future state of human center of mass (CoM) to improve the stability of user with wearable devices. However, current methods for CoM trajectory prediction, especially with multi-modal data fusion are difficult to apply to this prediction task due to the high computational cost. This article addressed the problem of estimation and prediction of CoM under daily walking conditions and proposed a method to solve the above problems, which enable to deploy on wearable robots with limited computation resources. In our study, a visual-inertial-odometry

关键词： Trajectory Legged locomotion History Cameras Vectors Wearable devices Feature extraction Estimation Wearable robots Sensors

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Research on Efficient Network Switching and Load Balancing Algorithm Based on Ad-Hoc Network

Research on Efficient Network Switching and Load Balancing A...

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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Wu, Wanqing Dong, He He, Chenglong State Key Laboratory of Satellite Navigation System and Equipment Technology The 54th Research Institute of CETC Shijiazhuang050081 China

ISBN: (纸本)9789811966125

Aiming at the problems of poor scalability of Ad-Hoc flat network and unbalanced load in clustered network, we proposed an efficient dynamic network switching and load balancing algorithm based on Ad-Hoc network. First, we initialize the network and quickly build the initial two-layer network topology. Then, according to the state of the network, the node mode is dynamically converted. Finally, according to the load situation of the upper nodes in the network, the network topology is dynamically adjusted to achieve network load balancing. The experimental results show that the network topology establishment period is 20 s, the single node access time is 2 s, the average mode transition delay of cluster member-cluster head is 1.993 s, the average mode transition delay of cluster head-cluster member is 1.142 s, and the network topology dynamic maintenance period does not exceed 2 s. Therefore, the algorithm we proposed can quickly build a two-layer network topology, has efficient dynamic network switching capability and can achieve load balancing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Network topology

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A review of solar concentration technology applications in deep space exploration: Environmental adaptability and performance comparison

Space Solar Power and Wireless Transmission

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Space Solar Power and Wireless Transmission 2025年第1期2卷 43-53页

作者： Yanlong Zhang Pengzhen Guo Mengfan Tian Huazhi Chen Rongqiang Liu Zongquan Deng Lifang Li National Key Laboratory of Aerospace Mechanism Harbin Institute of Technology Harbin 150001 PR China National Key Laboratory of Laser Spatial Information Harbin Institute of Technology Harbin 150001 PR China National Key Laboratory of Space Environment and Matter Behaviors Harbin Institute of Technology Harbin 150001 PR China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin 150001 PR China

Deep space exploration missions and the construction of planetary research stations impose strict demands on energy self-sufficiency systems. Solar energy, due to its abundant availability and sustainability, has become the preferred solution. However, extreme environmental conditions in space – including drastic temperature fluctuations, vacuum environments, high-energy particles, and intense radiation – pose significant challenges to the performance and lifespan of solar energy systems. Concentration technology, which enhances photoelectric and photothermal conversion efficiency by focusing sunlight, is crucial for space missions. This review examines the primary environmental factors affecting the performance of solar concentrators, including solar irradiance, thermal cycling, vacuum-induced outgassing, radiation effects, and impacts from micrometeoroids and orbital debris. The analysis focuses on three types of high-temperature concentrators: Fresnel lenses, Scheffler concentrators, and parabolic dish concentrators. Fresnel lenses are characterized by low cost and simple structure but are susceptible to optical degradation at high temperatures. Scheffler concentrators utilize geometric optimization to improve uniformity of light distribution, while parabolic dish concentrators achieve high optical efficiency, making them suitable for high-energy applications though requiring precise solar tracking. Performance comparisons in the thermal power range of 0 to 25 kW reveal that parabolic dish concentrators excel in high-power scenarios with greater efficiency and smaller aperture sizes. Conversely, Fresnel lenses and Scheffler concentrators are more effective in medium to low-temperature applications. Based on these findings, this review emphasizes the need to select concentrators according to mission requirements and outlines future research directions. These include the development of advanced materials, optimized optical designs, and improvements in system integr

关键词： Deep space exploration Solar concentrators Extreme environmental conditions

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A ResNet-based approach for accurate radiographic diagnosis of knee osteoarthritis

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CAAI Transactions on Intelligence technology 2022年第3期7卷 512-521页

作者： Yu Wang Shibo Li Baoliang Zhao Jianwei Zhang Yuanyuan Yang Bing Li School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhenChina Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhenChina TAMS Department of InformaticsUniversity of HamburgHamburgGermany

Currently X-ray images are clinically graded by experienced clinicians using the Kellgren and Lawrence(KL)scoring ***,individual scoring is subjective and error *** study proposes an approach for automated knee osteoarthritis classification based on deep neural *** knee X-ray images are first preprocessed with frequency-domain filtering and histogram normalisation,making the trabecular bone texture more obvious and benefiting the subsequent classification ***,a two-step classification strategy is proposed by extracting the joint centre based on the VGG network and classifying osteoarthritis grades based on the ResNet-50 *** addition,a rebalance operation is proposed to deal with the dataset unbalance problem,and a quick search technique is proposed to improve the iterative search efficiency for the joint *** all of these techniques,a classification accuracy of 81.41%is obtained,which is higher compared to the state-of-the-art approaches.

关键词： AMSGrad knee osteoarthritis recognition ResNet VGG

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