Editorial

4 pages, 194 KiB

Open AccessEditorial

Future AI and Robotics: Visual and Spatial Perception Enhancement and Reasoning

by Wenfeng Zheng, Mingzhe Liu, Chao Liu and Dan Wang

Electronics 2023, 12(23), 4787; https://doi.org/10.3390/electronics12234787 - 26 Nov 2023

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Over the past several decades, artificial intelligence (AI) has been tremendously boosted by new algorithm designs, exponentially increased computing power, and an immense volume of calculation materials (i [...] Full article

(This article belongs to the Special Issue Future AI and Robotics: Visual- and Spatial-Based Perception Enhancement and Reasoning)

Research

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21 pages, 12584 KiB

Open AccessArticle

Attention-Guided HDR Reconstruction for Enhancing Smart City Applications

by Yung-Yao Chen, Chih-Hsien Hsia, Sin-Ye Jhong and Chin-Feng Lai

Electronics 2023, 12(22), 4625; https://doi.org/10.3390/electronics12224625 - 12 Nov 2023

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Abstract

In the context of smart city development, video surveillance serves as a critical component for maintaining public safety and operational efficiency. However, traditional surveillance systems are often constrained by a limited dynamic range, leading to the loss of essential image details. To address [...] Read more.

In the context of smart city development, video surveillance serves as a critical component for maintaining public safety and operational efficiency. However, traditional surveillance systems are often constrained by a limited dynamic range, leading to the loss of essential image details. To address this limitation, this paper introduces HDRFormer, an innovative framework designed to enhance high dynamic range (HDR) image quality in edge–cloud-based video surveillance systems. Leveraging advanced deep learning algorithms and Internet of Things (IoT) technology, HDRFormer employs a unique architecture comprising a feature extraction module (FEM) and a weighted attention module (WAM). The FEM leverages a transformer-based hierarchical structure to adeptly capture multi-scale image information. In addition, the guided filters are utilized to steer the network, thereby enhancing the structural integrity of the images. On the other hand, the WAM focuses on reconstructing saturated areas, improving the perceptual quality of the images, and rendering the reconstructed HDR images with naturalness and color saturation. Extensive experiments on multiple HDR image reconstruction datasets demonstrate HDRFormer’s substantial improvements, achieving up to a 2.7 dB increase in the peak signal-to-noise ratio (PSNR) and an enhancement of 0.09 in the structural similarity (SSIM) compared to existing methods. In addition, the framework exhibits outstanding performance in multi-scale structural similarity (MS-SSIM) and HDR visual difference predictor (HDR-VDP2.2). The proposed method not only outperforms the existing HDR reconstruction techniques but also offers better generalization capabilities, laying a robust foundation for future applications in smart cities. Full article

(This article belongs to the Special Issue Future AI and Robotics: Visual- and Spatial-Based Perception Enhancement and Reasoning)

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19 pages, 8760 KiB

Open AccessArticle

Three-Dimensional Point Cloud Reconstruction Method of Cardiac Soft Tissue Based on Binocular Endoscopic Images

by Jiawei Tian, Botao Ma, Siyu Lu, Bo Yang, Shan Liu and Zhengtong Yin

Electronics 2023, 12(18), 3799; https://doi.org/10.3390/electronics12183799 - 08 Sep 2023

Cited by 1 | Viewed by 893

Abstract

Three-dimensional reconstruction technology based on binocular stereo vision is a key research area with potential clinical applications. Mainstream research has focused on sparse point reconstruction within the soft tissue domain, limiting the comprehensive 3D data acquisition required for effective surgical robot navigation. This [...] Read more.

Three-dimensional reconstruction technology based on binocular stereo vision is a key research area with potential clinical applications. Mainstream research has focused on sparse point reconstruction within the soft tissue domain, limiting the comprehensive 3D data acquisition required for effective surgical robot navigation. This study introduces a new paradigm to address existing challenges. An innovative stereoscopic endoscopic image correction algorithm is proposed, exploiting intrinsic insights into stereoscopic calibration parameters. The synergy between the stereoscopic endoscope parameters and the disparity map derived from the cardiac soft tissue images ultimately leads to the acquisition of precise 3D points. Guided by deliberate filtering and optimization methods, the triangulation process subsequently facilitates the reconstruction of the complex surface of the cardiac soft tissue. The experimental results strongly emphasize the accuracy of the calibration algorithm, confirming its utility in stereoscopic endoscopy. Furthermore, the image rectification algorithm exhibits a significant reduction in vertical parallax, which effectively enhances the stereo matching process. The resulting 3D reconstruction technique enables the targeted surface reconstruction of different regions of interest in the cardiac soft tissue landscape. This study demonstrates the potential of binocular stereo vision-based 3D reconstruction techniques for integration into clinical settings. The combination of joint calibration algorithms, image correction innovations, and precise tissue reconstruction enhances the promise of improved surgical precision and outcomes in the field of cardiac interventions. Full article

(This article belongs to the Special Issue Future AI and Robotics: Visual- and Spatial-Based Perception Enhancement and Reasoning)

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17 pages, 8069 KiB

Open AccessArticle

Dose Images Reconstruction Based on X-ray-Induced Acoustic Computed Tomography

by Yanhua Liu, Mingzhe Liu, Xin Jiang, Xianghe Liu and Min Liu

Electronics 2023, 12(10), 2241; https://doi.org/10.3390/electronics12102241 - 15 May 2023

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Abstract

The accurate reconstruction of the in vivo dose is a critical step in radiation therapy. X-ray-induced acoustic imaging is a promising technology for in vivo dose reconstruction, as it enables the nonradiative and noninvasive monitoring of radiation dose. However, current X-ray acoustic imaging [...] Read more.

The accurate reconstruction of the in vivo dose is a critical step in radiation therapy. X-ray-induced acoustic imaging is a promising technology for in vivo dose reconstruction, as it enables the nonradiative and noninvasive monitoring of radiation dose. However, current X-ray acoustic imaging methods suffer from several limitations, including high signal-to-noise ratio, poor imaging quality and massive loss of structural information. To address these limitations, we propose a dose image reconstruction method based on tensor sparse dictionary learning. Specifically, we combine tensor coding with compressed sensing data, extend two-dimensional dictionary learning to three-dimensional by using tensor product, and then utilize the spatial information of X-ray acoustic signal more efficiently. To reduce the artifacts of reconstruction images caused by spare sampling, we design the alternate iterative solution of the tensor sparse coefficient and tensor dictionary. In addition, we build the X-ray-induced acoustic dose images reconstruction system, simulate the X-ray acoustic signals based on patients’ information from Sichuan Cancer Hospital, and then create the simulated datasets. Compared to some typical state-of-the art imaging methods, the experimental results demonstrate that our method can significantly improve the quality of reconstructed images and the accuracy of dose distribution. Full article

(This article belongs to the Special Issue Future AI and Robotics: Visual- and Spatial-Based Perception Enhancement and Reasoning)

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15 pages, 3118 KiB

Open AccessArticle

Heterogeneous Quasi-Continuous Spiking Cortical Model for Pulse Shape Discrimination

by Runxi Liu, Haoran Liu, Bo Yang, Borui Gu, Zhengtong Yin and Shan Liu

Electronics 2023, 12(10), 2234; https://doi.org/10.3390/electronics12102234 - 14 May 2023

Viewed by 1092

Abstract

The present study introduces the heterogeneous quasi-continuous spiking cortical model (HQC-SCM) method as a novel approach for neutron and gamma-ray pulse shape discrimination. The method utilizes specific neural responses to extract features in the falling edge and delayed fluorescence parts of radiation pulse [...] Read more.

The present study introduces the heterogeneous quasi-continuous spiking cortical model (HQC-SCM) method as a novel approach for neutron and gamma-ray pulse shape discrimination. The method utilizes specific neural responses to extract features in the falling edge and delayed fluorescence parts of radiation pulse signals. In addition, the study investigates the contributions of HQC-SCM’s parameters to its discrimination performance, leading to the development of an automatic parameter selection strategy. As HQC-SCM is a chaotic system, a genetic algorithm-based parameter optimization method was proposed to locate local optima of HQC-SCM’s parameter solutions efficiently and robustly in just a few iterations of evolution. The experimental results of this study demonstrate that the HQC-SCM method outperforms traditional and state-of-the-art pulse shape discrimination algorithms, including falling edge percentage slope, zero crossing, charge comparison, frequency gradient analysis, pulse-coupled neural network, and ladder gradient methods. The outstanding discrimination performance of HQC-SCM enables plastic scintillators to compete with liquid and crystal scintillators’ neutron and gamma-ray pulse shape discrimination ability. Additionally, the HQC-SCM method outperforms other methods when dealing with noisy radiation pulse signals. Therefore, it is an effective and robust approach that can be applied in radiation detection systems across various fields. Full article

(This article belongs to the Special Issue Future AI and Robotics: Visual- and Spatial-Based Perception Enhancement and Reasoning)

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13 pages, 527 KiB

Open AccessCommunication

Energy-Based MRI Semantic Augmented Segmentation for Unpaired CT Images

by Shengliang Cai, Chuyun Shen and Xiangfeng Wang

Electronics 2023, 12(10), 2174; https://doi.org/10.3390/electronics12102174 - 10 May 2023

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Abstract

The multimodal segmentation of medical images is essential for clinical applications as it allows medical professionals to detect anomalies, monitor treatment effectiveness, and make informed therapeutic decisions. However, existing segmentation methods depend on paired images of modalities, which may not always be available [...] Read more.

The multimodal segmentation of medical images is essential for clinical applications as it allows medical professionals to detect anomalies, monitor treatment effectiveness, and make informed therapeutic decisions. However, existing segmentation methods depend on paired images of modalities, which may not always be available in practical scenarios, thereby limiting their applicability. To address this challenge, current approaches aim to align modalities or generate missing modality images without a ground truth, which can introduce irrelevant texture details. In this paper, we propose the energy-basedsemantic augmented segmentation (ESAS) model, which employs the energy of latent semantic features from a supporting modality to enhance the segmentation performance on unpaired query modality data. The proposed ESAS model is a lightweight and efficient framework suitable for most unpaired multimodal image-learning tasks. We demonstrate the effectiveness of our ESAS model on the MM-WHS 2017 challenge dataset, where it significantly improved Dice accuracy for cardiac segmentation on CT volumes. Our results highlight the potential of the proposed ESAS model to enhance patient outcomes in clinical settings by providing a promising approach for unpaired multimodal medical image segmentation tasks. Full article

(This article belongs to the Special Issue Future AI and Robotics: Visual- and Spatial-Based Perception Enhancement and Reasoning)

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21 pages, 10391 KiB

Open AccessFeature PaperArticle

A Novel Architecture of a Six Degrees of Freedom Parallel Platform

by Qiuxiang Gu, Jiawei Tian, Bo Yang, Mingzhe Liu, Borui Gu, Zhengtong Yin, Lirong Yin and Wenfeng Zheng

Electronics 2023, 12(8), 1774; https://doi.org/10.3390/electronics12081774 - 09 Apr 2023

Cited by 49 | Viewed by 2442

Abstract

With the rapid development of the manufacturing industry, industrial automation equipment represented by computer numerical control (CNC) machine tools has put forward higher and higher requirements for the machining accuracy of parts. Compared with the multi-axis serial platform solution, the parallel platform solution [...] Read more.

With the rapid development of the manufacturing industry, industrial automation equipment represented by computer numerical control (CNC) machine tools has put forward higher and higher requirements for the machining accuracy of parts. Compared with the multi-axis serial platform solution, the parallel platform solution is theoretically more suitable for high-precision machining equipment. There are many parallel platform solutions, but not one can provide a common physical platform to test the effectiveness of a variety of control algorithms. To achieve the goals, this paper is based on the Stewart six degrees of freedom parallel platform, and it mainly studies the platform construction. This study completed the mechanical structure design of the parallel platform. Based on the microprogrammed control unit (MCU) + pre-driver chip + three-phase full bridge solution, we have completed the circuit design of the motor driver. We wrote the program of MCU to drive six parallel robotic arms as well as the program of the parallel platform control center on the PC, and we completed the system joint debugging. The closed-loop control effect of the parallel platform workspace pose is realized. Full article

(This article belongs to the Special Issue Future AI and Robotics: Visual- and Spatial-Based Perception Enhancement and Reasoning)

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12 pages, 1215 KiB

Open AccessArticle

Generalized Knowledge Distillation for Unimodal Glioma Segmentation from Multimodal Models

by Feng Xiong, Chuyun Shen and Xiangfeng Wang

Electronics 2023, 12(7), 1516; https://doi.org/10.3390/electronics12071516 - 23 Mar 2023

Cited by 4 | Viewed by 1315

Abstract

Gliomas, primary brain tumors arising from glial cells, can be effectively identified using Magnetic Resonance Imaging (MRI), a widely employed diagnostic tool in clinical settings. Accurate glioma segmentation, which is crucial for diagnosis and surgical intervention, can be achieved by integrating multiple MRI [...] Read more.

Gliomas, primary brain tumors arising from glial cells, can be effectively identified using Magnetic Resonance Imaging (MRI), a widely employed diagnostic tool in clinical settings. Accurate glioma segmentation, which is crucial for diagnosis and surgical intervention, can be achieved by integrating multiple MRI modalities that offer complementary information. However, limited access to multiple modalities in certain clinical contexts often results in suboptimal performance of glioma segmentation methods. This study introduces a novel generalized knowledge distillation framework designed to transfer multimodal knowledge from a teacher model to a unimodal student model via two distinct distillation strategies: segmentation graph distillation and cascade region attention distillation. The former enables the student to replicate the teacher’s softened output, whereas the latter facilitates extraction and learning of region feature information at various levels within the teacher model. Our evaluation of the proposed distillation strategies using the BraTS 2018 dataset confirms their superior performance in unimodal segmentation contexts compared with existing methods. Full article

(This article belongs to the Special Issue Future AI and Robotics: Visual- and Spatial-Based Perception Enhancement and Reasoning)

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