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Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images.

Research & information: general --- depthwise separable convolution (DSC) --- all convolutional network (ACN) --- batch normalization (BN) --- ensemble convolutional neural network (ECNN) --- electrocardiogram (ECG) --- MIT-BIH database --- cephalometric landmark --- X-ray --- deep learning --- ResNet --- registration --- electronic human-machine interface --- blindness --- gesture recognition --- inertial sensors --- IMU --- dynamic contrast-enhanced MRI --- kidney perfusion --- glomerular filtration rate --- pharmacokinetic modeling --- multi-layer perceptron --- parameter estimation --- instance segmentation --- computer vision --- retinal blood vessel image --- computer-aided diagnosis --- U-shaped neural network --- residual learning --- semantic gap --- intracranial hemorrhage --- computed tomography --- random forest --- sleep disorder --- obstructive sleep disorder --- overnight polysomnogram --- EEG --- EMG --- ECG --- HRV signals --- Electronic Medical Record (EMR) --- disease prediction --- Amyotrophic Lateral Sclerosis (ALS) --- weighted Jaccard index (WJI) --- lung cancer --- CT images --- CNN --- pulmonary fibrosis --- radiotherapy --- depthwise separable convolution (DSC) --- all convolutional network (ACN) --- batch normalization (BN) --- ensemble convolutional neural network (ECNN) --- electrocardiogram (ECG) --- MIT-BIH database --- cephalometric landmark --- X-ray --- deep learning --- ResNet --- registration --- electronic human-machine interface --- blindness --- gesture recognition --- inertial sensors --- IMU --- dynamic contrast-enhanced MRI --- kidney perfusion --- glomerular filtration rate --- pharmacokinetic modeling --- multi-layer perceptron --- parameter estimation --- instance segmentation --- computer vision --- retinal blood vessel image --- computer-aided diagnosis --- U-shaped neural network --- residual learning --- semantic gap --- intracranial hemorrhage --- computed tomography --- random forest --- sleep disorder --- obstructive sleep disorder --- overnight polysomnogram --- EEG --- EMG --- ECG --- HRV signals --- Electronic Medical Record (EMR) --- disease prediction --- Amyotrophic Lateral Sclerosis (ALS) --- weighted Jaccard index (WJI) --- lung cancer --- CT images --- CNN --- pulmonary fibrosis --- radiotherapy

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Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images.

Research & information: general --- depthwise separable convolution (DSC) --- all convolutional network (ACN) --- batch normalization (BN) --- ensemble convolutional neural network (ECNN) --- electrocardiogram (ECG) --- MIT-BIH database --- cephalometric landmark --- X-ray --- deep learning --- ResNet --- registration --- electronic human-machine interface --- blindness --- gesture recognition --- inertial sensors --- IMU --- dynamic contrast-enhanced MRI --- kidney perfusion --- glomerular filtration rate --- pharmacokinetic modeling --- multi-layer perceptron --- parameter estimation --- instance segmentation --- computer vision --- retinal blood vessel image --- computer-aided diagnosis --- U-shaped neural network --- residual learning --- semantic gap --- intracranial hemorrhage --- computed tomography --- random forest --- sleep disorder --- obstructive sleep disorder --- overnight polysomnogram --- EEG --- EMG --- ECG --- HRV signals --- Electronic Medical Record (EMR) --- disease prediction --- Amyotrophic Lateral Sclerosis (ALS) --- weighted Jaccard index (WJI) --- lung cancer --- CT images --- CNN --- pulmonary fibrosis --- radiotherapy --- n/a

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Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management.

pig weight --- body size --- estimation --- deep learning --- convolutional neural network --- pig identification --- mask scoring R-CNN --- soft-NMS --- group-housed pigs --- audio --- dairy cow --- mastication --- jaw movement --- forage management --- precision livestock management --- equine behavior --- wearable sensor --- intermodality interaction --- class-balanced focal loss --- absorbing Markov chain --- cow behavior analysis --- prediction of calving time --- cow identification --- EfficientDet --- YOLACT++ --- cascaded model --- instance segmentation --- generative adversarial network --- machine learning --- automated medical image processing --- deep neural network --- animal science --- CT scans --- computer vision --- cow --- extensive livestock --- sensorized wearable device --- monitoring --- parturition prediction --- radar sensors --- radar signal processing --- animal farming --- computational ethology --- signal classification --- wavelet analysis --- dairy welfare --- hierarchical clustering --- mutual information --- precision livestock farming --- time budgets --- unsupervised machine learning --- wearables design --- animal-centered design --- animal telemetry --- modularity --- smart collar --- design contributions --- additive manufacturing --- low-frequency tracking --- commercial aviary --- laying hens --- false registrations --- tree-based classifier --- animal behaviour

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Electrification, automation of vehicle control, digitalization and new mobility are the mega-trends in automotive engineering, and they are strongly connected. While many demonstrations for highly automated vehicles have been made worldwide, many challenges remain in bringing automated vehicles to the market for private and commercial use. The main challenges are as follows: reliable machine perception; accepted standards for vehicle-type approval and homologation; verification and validation of the functional safety, especially at SAE level 3+ systems; legal and ethical implications; acceptance of vehicle automation by occupants and society; interaction between automated and human-controlled vehicles in mixed traffic; human–machine interaction and usability; manipulation, misuse and cyber-security; the system costs of hard- and software and development efforts. This Special Issue was prepared in the years 2021 and 2022 and includes 15 papers with original research related to recent advances in the aforementioned challenges. The topics of this Special Issue cover: Machine perception for SAE L3+ driving automation; Trajectory planning and decision-making in complex traffic situations; X-by-Wire system components; Verification and validation of SAE L3+ systems; Misuse, manipulation and cybersecurity; Human–machine interactions, driver monitoring and driver-intention recognition; Road infrastructure measures for the introduction of SAE L3+ systems; Solutions for interactions between human- and machine-controlled vehicles in mixed traffic.

Technology: general issues --- History of engineering & technology --- automated driving --- scenario-based testing --- software framework --- traffic signs --- ADAS --- traffic sign recognition system --- cooperative perception --- ITS --- digital twin --- sensor fusion --- edge cloud --- autonomous drifting --- model predictive control (MPC) --- successive linearization --- adaptive control --- vehicle motion control --- varying road surfaces --- vehicle dynamics --- Mask R-CNN --- transfer learning --- inverse gamma correction --- illumination --- instance segmentation --- pedestrian custom dataset --- deep learning --- wheel loaders --- throttle prediction --- state prediction --- automation --- safety validation --- automated driving systems --- decomposition --- modular safety approval --- modular testing --- fault tree analysis --- adaptive cruise control --- informed machine learning --- physics-guided reinforcement learning --- safety --- autonomous vehicles --- autonomous conflict management --- UTM --- UAV --- UGV --- U-Space --- framework development --- lane detection --- simulation and modelling --- multi-layer perceptron --- convolutional neural network --- driver drowsiness --- ECG signal --- heart rate variability --- wavelet scalogram --- automated driving (AD) --- driving simulator --- expression of trust --- acceptance --- simulator case study --- NASA TLX --- advanced driver assistant systems (ADAS) --- system usability scale --- driving school --- virtual validation --- ground truth --- reference measurement --- calibration method --- simulation --- traffic evaluation --- simulation and modeling --- connected and automated vehicle --- driver assistance system --- virtual test and validation --- radar sensor --- physical perception model --- virtual sensor model --- automated driving --- scenario-based testing --- software framework --- traffic signs --- ADAS --- traffic sign recognition system --- cooperative perception --- ITS --- digital twin --- sensor fusion --- edge cloud --- autonomous drifting --- model predictive control (MPC) --- successive linearization --- adaptive control --- vehicle motion control --- varying road surfaces --- vehicle dynamics --- Mask R-CNN --- transfer learning --- inverse gamma correction --- illumination --- instance segmentation --- pedestrian custom dataset --- deep learning --- wheel loaders --- throttle prediction --- state prediction --- automation --- safety validation --- automated driving systems --- decomposition --- modular safety approval --- modular testing --- fault tree analysis --- adaptive cruise control --- informed machine learning --- physics-guided reinforcement learning --- safety --- autonomous vehicles --- autonomous conflict management --- UTM --- UAV --- UGV --- U-Space --- framework development --- lane detection --- simulation and modelling --- multi-layer perceptron --- convolutional neural network --- driver drowsiness --- ECG signal --- heart rate variability --- wavelet scalogram --- automated driving (AD) --- driving simulator --- expression of trust --- acceptance --- simulator case study --- NASA TLX --- advanced driver assistant systems (ADAS) --- system usability scale --- driving school --- virtual validation --- ground truth --- reference measurement --- calibration method --- simulation --- traffic evaluation --- simulation and modeling --- connected and automated vehicle --- driver assistance system --- virtual test and validation --- radar sensor --- physical perception model --- virtual sensor model

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The reprint focuses on artificial intelligence-based learning approaches and their applications in remote sensing fields. The explosive development of machine learning, deep learning approaches and its wide applications in signal processing have been witnessed in remote sensing. The new developments in remote sensing have led to a high resolution monitoring of ground on a global scale, giving a huge amount of ground observation data. Thus, artificial intelligence-based deep learning approaches and its applied signal processing are required for remote sensing. These approaches can be universal or specific tools of artificial intelligence, including well known neural networks, regression methods, decision trees, etc. It is worth compiling the various cutting-edge techniques and reporting on their promising applications.

Technology: general issues --- History of engineering & technology --- Environmental science, engineering & technology --- pine wilt disease dataset --- GIS application visualization --- test-time augmentation --- object detection --- hard negative mining --- video synthetic aperture radar (SAR) --- moving target --- shadow detection --- deep learning --- false alarms --- missed detections --- synthetic aperture radar (SAR) --- on-board --- ship detection --- YOLOv5 --- lightweight detector --- remote sensing image --- spectral domain translation --- generative adversarial network --- paired translation --- synthetic aperture radar --- ship instance segmentation --- global context modeling --- boundary-aware box prediction --- land-use and land-cover --- built-up expansion --- probability modelling --- landscape fragmentation --- machine learning --- support vector machine --- frequency ratio --- fuzzy logic --- artificial intelligence --- remote sensing --- interferometric phase filtering --- sparse regularization (SR) --- deep learning (DL) --- neural convolutional network (CNN) --- semantic segmentation --- open data --- building extraction --- unet --- deeplab --- classifying-inversion method --- AIS --- atmospheric duct --- ship detection and classification --- rotated bounding box --- attention --- feature alignment --- weather nowcasting --- ResNeXt --- radar data --- spectral-spatial interaction network --- spectral-spatial attention --- pansharpening --- UAV visual navigation --- Siamese network --- multi-order feature --- MIoU --- imbalanced data classification --- data over-sampling --- graph convolutional network --- semi-supervised learning --- troposcatter --- tropospheric turbulence --- intercity co-channel interference --- concrete bridge --- visual inspection --- defect --- deep convolutional neural network --- transfer learning --- interpretation techniques --- weakly supervised semantic segmentation --- n/a